Cooperative Communications and Networking: Technologies and System Design 1441971939, 9781441971937

Table of contents :
Foreword
Preface
Contents
Acronyms
Chapter 1
Introduction
1.1 An Overview on Cooperative Communications
1.2 Brief History of Cooperative and Relay Channels
1.3 Standardization of Cooperative Communication and Relay Technology
1.4 Book Outline
References
Chapter 2
Review of Wireless Communications and MIMO Techniques
2.1 Characteristics of Wireless Channels
2.1.1 Path Loss
2.1.2 Shadowing Effect
2.1.3 Multipath Fading
2.2 Techniques to Exploit Spatial Diversity
2.2.1 Single-Input Multiple-Output (SIMO) System
2.2.2 Multiple-Input Single-Output (MISO) System
2.2.3 Multiple-Input Multiple-Output (MIMO) System
2.3 Capacity of Wireless Channels
2.3.1 Capacity of AWGN Channels
2.3.2 Capacity of Flat Fading Channels
2.3.3 Capacity with Multiple Antennas
2.4 Diversity-and-Multiplexing Tradeoff
References
Chapter 3
Two-User Cooperative Transmission Schemes
3.1 Decode-and-Forward Relaying Schemes
3.1.1 Basic DF Relaying Scheme
3.1.2 Selection DF Relaying Scheme
3.1.3 Demodulate-and-Forward Relaying Scheme
3.2 Amplify-and-Forward Relaying Schemes
3.2.1 Basic AF Relaying Scheme
3.2.2 Incremental AF Relaying Scheme
3.3 Coded Cooperation
3.3.1 Basic Coded Cooperation Scheme
3.3.2 User Multiplexing for Coded Cooperation
3.4 Compress-and-Forward Relaying Schemes
3.5 Channel Estimation in Single Relay Systems
References
Chapter 4
Cooperative Transmission Schemes with Multiple Relays
4.1 Orthogonal Cooperation
4.1.1 Orthogonal Cooperation with AF Relays
4.1.2 Orthogonal Cooperation with DF Relays
4.2 Transmit Beamforming
4.2.1 Transmit Beamforming with AF Relays
4.2.2 Transmit Beamforming with DF Relays
4.3 Selective Relaying
4.3.1 Selective Relaying with AF Relays
4.3.2 Selective Relaying with DF Relays
4.4 Distributed Space-Time Coding (DSTC)
4.4.1 Distributed Space-Time Coding with DF Relays
4.4.2 Distributed Space-Time Coding with AF Relays
4.5 Channel Estimation in Multi-Relay Systems
4.5.1 Training Design for AF Multi-Relay Systems
4.5.2 Training Design for DF Multi-Relay Systems
4.6 Other Topics on Multi-Relay Cooperative Communications
4.6.1 Multi-Hop Cooperative Transmissions
4.6.2 Asynchronous Cooperative Transmissions
References
Chapter 5
Fundamental Limits of Cooperative and Relay Networks
5.1 Gaussian Relay Channels
5.1.1 Cut-Set Bound of Gaussian Relay Channels
5.1.2 Decode-and-Forward and Degraded Relay Channels
5.1.3 Compress-and-Forward
5.2 Single-Relay Fading Channels
5.2.1 Ergodic Capacity
5.2.2 Diversity and Multiplexing Tradeoffs
5.3 Multi-Relay Networks
5.3.1 Upper Bound of Gaussian Multi-Relay Networks
5.3.2 Lower bound of Gaussian Multi-Relay Networks and Asymptotic Capacity Results
5.3.3 Multi-Relay Fading Channels
Appendix 5.1 Proof of Theorem 5.12
References
Chapter 6
Cooperative Communications with Multiple Sources
6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA)
6.1.1 Round-Robin Scheduling
6.1.2 Opportunistic Scheduling
6.2 Code-Division Multiple Access (CDMA)
6.2.1 Uplink CDMA with Designated Relays
6.2.2 Uplink CDMA with Shared Relays
6.3 Space-Division Multiple Access (SDMA)
6.4 Partner Selection Strategies
6.4.1 Centralized Partner Selection Strategy
6.4.2 Decentralized Partner Selection Strategy
References
Chapter 7
Cooperation Relaying in OFDM and MIMO Systems
7.1 Brief Review of OFDM Systems
7.2 Resource Allocation in Pair-Wise Cooperative OFDM Systems
7.2.1 Power Allocation in Pair-Wise Cooperative OFDM Systems
7.2.2 Subcarrier Matching for Pair-Wise Cooperative OFDM Systems
7.3 Cooperative OFDM Systems with Multiple Relays
7.3.1 Cooperative Beamforming for OFDM Multi-Relay Systems
7.3.2 Selective Relaying for OFDM Multi-Relay Systems
7.4 Distributed Space-Frequency Codes
7.4.1 Decode-and-Forward Space-Frequency Codes
7.4.2 Amplify-and-Forward Space-Frequency Codes
7.5 Cooperation with MIMO Relays
References
Chapter 8
Medium Access Control in Cooperative Networks
8.1 Cooperation with Slotted ALOHA
8.1.1 Definition of Stability Region
8.1.2 Stability Region of a Cooperative Pair
8.2 Collision Resolution Mechanisms in Cooperative Networks
8.2.1 Network-Assisted Diversity Multiple Access (NDMA)
8.2.2 Enhancements to NDMA with Relaying Users
8.3 Cooperation with CSMA/CA
8.3.1 Overview of the IEEE 802.11 MAC Protocol
8.3.2 CoopMAC based on the IEEE 802.11 Protocol
8.3.3 Analysis of CoopMAC
8.4 Automatic Retransmission reQuest (ARQ) with Cooperative Relays
8.5 Throughput Optimal Scheduling Protocols for Cooperative Networks
8.5.1 Review of Throughput Optimal Control Policy for Non-Cooperative Networks
8.5.2 Throughput Optimal Control Policy for Cooperative Networks
Appendix 8.1: Collision and Transmission Probabilities in IEEE 802.11 Systems
Appendix 8.2: Derivation of Probabilities
References
Chapter 9
Networking and Cross-Layer Issues in Cooperative Networks
9.1 QoS in Cooperative Networks
9.1.1 QoS of a Simple Relay Network
9.1.2 QoS of a Cooperative Pair
9.2 Routing in Cooperative Networks
9.2.1 General Formulation of Cooperative Routing
9.2.2 Heuristic Algorithms for Cooperative Routing
9.3 Security Issues in Cooperative Networks
9.3.1 Misbehavior in Relay Networks
9.3.2 Security in Single-Relay Cooperative Networks
9.3.3 Security in Multi-Relay Cooperative Networks
References
Index

Citation preview

Cooperative Communications and Networking

Y.-W. Peter Hong • Wan-Jen Huang C.-C. Jay Kuo

Cooperative Communications and Networking Technologies and System Design Foreword by Georgios B. Giannakis

1C

Y.-W. Peter Hong National Tsing Hua University Department of Electrical Engineering Kuang-Fu Rd. Section 2 101 30013 Hsinchu Taiwan R.O.C. [email protected]

C.-C. Jay Kuo University of Southern California Viterbi School of Engineering McClintock Ave. 3740 90089-2564 Los Angeles California USA [email protected]

Wan-Jen Huang National Sun Yat-Sen University Lien-Hai Road 70 80424 Kaohsiung Institute of Comm. Engin. Taiwan R.O.C. [email protected]

ISBN 978-1-4419-7193-7 e-ISBN 978-1-4419-7194-4 DOI 10.1007/978-1-4419-7194-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010932009 © Springer Science+Business Media, LLC 2010 Portions of text appearing on pages 85–87, 104–106, 109–112, 158–159, 163–177, 196–225, 247–258, 274–288, 295–303, 307–313, 317–329, 336–357, 366–369, 376–391 used with permission © IEEE. See references for further details. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

“To my beloved parents, and my lovely wife and daughter.” – Peter “To my beloved husband, Wei-Feng, and my parents, Ching-Song and Pi-Hsia.” – Wan-Jen “I would like to dedicate this book to my beloved parents, wife and daughter.” – Jay

Foreword

Over the past years, there has been a considerable research activity in the field of wireless cooperative communications and networking. By now, this field has reached a mature level and there is a definite need to organize research results and present them in a unified manner. This is precisely the objective and strength of this book: to deliver in a coherent and consistent story the stateof-the-art in cooperative communications and networking. This grueling effort will benefit graduate students in advanced topics in wireless communications, active researchers to identify new directions, as well as designers to stay current for the latest findings. The authors of Cooperative Communications and Networking – Technologies and System Design have delivered extensive and fundamental results in this subject. From this vantage point, they were able to provide a timely and comprehensive view of this exciting topic. To allow for consistency and self-containment, the authors first review wireless communications basics, with special emphasis on diversity techniques, capacity, and design tradeoffs. Building on these necessary foundations, simple illustrative cooperation models are first analyzed followed by extensions comprising multiple relays and sources. The last part delineates how cooperation can be integrated with advanced wireless transmission technologies as well as how it permeates the benefits of cooperation at the physical layer to higher-layers of the protocol stack. The end result is a book that not only paves the road for newcomers in the classroom and industry, but also serves as reference material for active researchers and network designers in this field. Minnesota, USA, April 2, 2010

Georgios B. Giannakis

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Preface

Cooperative and relay communications have been one of the most widely explored topics in communications over the past few years. Although many underlying ideas originate from the early works on relay channels in the 1960’s, the technology has only recently been popularized by the works of Sendonaris, Erkip, and Aazhang as well as the works of Laneman, Tse, and Wornell in the late 1990’s and early 2000’s. The key idea is to have users cooperate in transmitting their messages to the destination, instead of operating independently and competing among each other for channel resources, as done in conventional networks. However, as the field has progressed over the years, cooperative communications have now become a design concept rather than a specific transmission technology. This concept has revolutionized the design of wireless networks, allowing us to increase coverage, throughput, and transmission reliability even as conventional transmission techniques gradually reach their limits. In recent years, cooperative and relay technologies have also made its way toward next generation wireless standards, such as IEEE 802.16 (WiMAX) or LTE, and have been incorporated into many modern wireless applications, such as cognitive radio and secret communications. With its expanding scope of applications, it is necessary for engineers and researchers to have a more fundamental understanding of the design concept, thus motivating our work on this book project. With this book, we hope to provide a more systematic way into learning this subject. Our interest in this topic initiated from our works on asynchronous and opportunistic cooperative transmission schemes presented in the early 2000’s. At that time, we proposed and analyzed the so-called opportunistic large arrays (OLA) system, where all users in the network can participate in the cooperation and relay the source’s message in an opportunistic and uncoordinated fashion, whenever they are able to reliably decode the message. Since then, we have also been interested in the use of cooperative communications in sensor networks, focusing on exploiting its energy efficiency and its ability to utilize sensor dependency to improve communication efficiency. In recent years, we have studied extensively the design of multiuser cooperative sys-

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tems, examining both physical and MAC layer issues such as relay-assisted multiuser detection schemes and cooperative MAC protocols. We have found this subject to be extremely interesting and have been attracted by its rich set of applications. Yet, we realize that with more and more researchers working on this problem, the technology is becoming rather advanced and diverse. However, with this book, we hope to connect the dots between these advanced topics and provide readers with a more comprehensive view of the subject. The aim of this book is to provide the basic concepts of cooperative communications and relay technology to enable engineers or graduate students to have a clear grasp of this growing field and have the basic knowledge to conduct advanced research and development in this area. The book is in no sense complete in terms of gathering all works in the field, but contains basic concepts that allow readers to gain a fundamental understanding of the system and of the challenges that it often encounters. The contents of the book can be summarized as follows: • In Chapter 1, we give a brief introduction of the various cooperation and relay techniques that will be introduced in later chapters. We also provide a brief history of this technology and a survey of its role in next generation wireless standards. • In Chapter 2, we review basic wireless communication and MIMO techniques for readers that are not familiar with this field. This knowledge is helpful in understanding the techniques to be introduced in other chapters of this book. • In Chapter 3, basic cooperative communication and relay techniques are introduced for a basic cooperative entity that consists of only two users and a common destination. • In Chapter 4, the cooperation and relay techniques are extended to systems with multiple relays. Here, the relays together form a distributed antenna array that can be used to exploit spatial diversity and multiplexing gains. • In Chapter 5, fundamental limits of cooperative and relay channels are described from the information-theoretic standpoint. • In Chapter 6, these techniques are applied to multiuser systems, where multiple cooperative entities may be transmitting simultaneously or where multiple sources may be accessing a common cooperative channel. • In Chapter 7, we describe how cooperative and relay technology can be integrated with other advanced wireless technology, such as OFDM and MIMO, and show how additional benefits can be obtained by doing so. • In Chapter 8, we describe how cooperative advantages exploited in the medium access control (MAC) layer and how MAC policies should be designed to enhance cooperative communications in the physical layer. • In Chapter 9, we introduce several cross-layer and networking issues that may arise in cooperative networks, including routing, QoS, and security considerations.

Preface

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The authors would like to send special thanks to research assistants Ms. Shu-Hsien Wang, Mr. Jui-Yang Chang, and also to colleague Dr. ShihChin Lin for their extensive efforts on the editing and proof-reading of this book. Without their help, we would never have been able to complete the manuscript. We would also like to thank Mr. Yung-Shun Wang, Ms. MiaoFeng Jian, and Ms. Chao-Wei Huang for their help on generating figures and illustrations, and also Mr. Meng-Hsi Chen, Mr. Ta-Yuan Liu, and Ms. Yi-Hua Lin for their help in proof-reading the manuscript. Hsinchu, Taiwan Kaohsiung, Taiwan Los Angeles, California, USA April, 2010

Y.-W. Peter Hong Wan-Jen Huang C.-C. Jay Kuo

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 An Overview on Cooperative Communications . . . . . . . . . . . . . 1 1.2 Brief History of Cooperative and Relay Channels . . . . . . . . . . . 5 1.3 Standardization of Cooperative Communication and Relay Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Book Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2

Review of Wireless Communications and MIMO Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Characteristics of Wireless Channels . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Shadowing Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Multipath Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Techniques to Exploit Spatial Diversity . . . . . . . . . . . . . . . . . . . . 2.2.1 Single-Input Multiple-Output (SIMO) System . . . . . . . 2.2.2 Multiple-Input Single-Output (MISO) System . . . . . . . 2.2.3 Multiple-Input Multiple-Output (MIMO) System . . . . 2.3 Capacity of Wireless Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Capacity of AWGN Channels . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Capacity of Flat Fading Channels . . . . . . . . . . . . . . . . . . 2.3.3 Capacity with Multiple Antennas . . . . . . . . . . . . . . . . . . . 2.4 Diversity-and-Multiplexing Tradeoff . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15 15 18 19 25 26 35 44 48 48 49 52 58 63

Two-User Cooperative Transmission Schemes . . . . . . . . . . . . . 3.1 Decode-and-Forward Relaying Schemes . . . . . . . . . . . . . . . . . . . . 3.1.1 Basic DF Relaying Scheme . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Selection DF Relaying Scheme . . . . . . . . . . . . . . . . . . . . . 3.1.3 Demodulate-and-Forward Relaying Scheme . . . . . . . . . . 3.2 Amplify-and-Forward Relaying Schemes . . . . . . . . . . . . . . . . . . .

67 67 68 78 81 87

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3.2.1 Basic AF Relaying Scheme . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Incremental AF Relaying Scheme . . . . . . . . . . . . . . . . . . . 3.3 Coded Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Basic Coded Cooperation Scheme . . . . . . . . . . . . . . . . . . 3.3.2 User Multiplexing for Coded Cooperation . . . . . . . . . . . 3.4 Compress-and-Forward Relaying Schemes . . . . . . . . . . . . . . . . . . 3.5 Channel Estimation in Single Relay Systems . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

88 100 102 103 106 114 115 120

4

Cooperative Transmission Schemes with Multiple Relays . 4.1 Orthogonal Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Orthogonal Cooperation with AF Relays . . . . . . . . . . . . 4.1.2 Orthogonal Cooperation with DF Relays . . . . . . . . . . . . 4.2 Transmit Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Transmit Beamforming with AF Relays . . . . . . . . . . . . . 4.2.2 Transmit Beamforming with DF Relays . . . . . . . . . . . . . 4.3 Selective Relaying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Selective Relaying with AF Relays . . . . . . . . . . . . . . . . . . 4.3.2 Selective Relaying with DF Relays . . . . . . . . . . . . . . . . . . 4.4 Distributed Space-Time Coding (DSTC) . . . . . . . . . . . . . . . . . . 4.4.1 Distributed Space-Time Coding with DF Relays . . . . . . 4.4.2 Distributed Space-Time Coding with AF Relays . . . . . . 4.5 Channel Estimation in Multi-Relay Systems . . . . . . . . . . . . . . . 4.5.1 Training Design for AF Multi-Relay Systems . . . . . . . . . 4.5.2 Training Design for DF Multi-Relay Systems . . . . . . . . . 4.6 Other Topics on Multi-Relay Cooperative Communications . . 4.6.1 Multi-Hop Cooperative Transmissions . . . . . . . . . . . . . . . 4.6.2 Asynchronous Cooperative Transmissions . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

125 126 127 133 135 135 141 146 147 150 153 153 161 168 168 173 178 178 187 190

5

Fundamental Limits of Cooperative and Relay Networks . . 5.1 Gaussian Relay Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Cut-Set Bound of Gaussian Relay Channels . . . . . . . . . . 5.1.2 Decode-and-Forward and Degraded Relay Channels . . . 5.1.3 Compress-and-Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Single-Relay Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Ergodic Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Diversity and Multiplexing Tradeoffs . . . . . . . . . . . . . . . . 5.3 Multi-Relay Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Upper Bound of Gaussian Multi-Relay Networks . . . . . 5.3.2 Lower bound of Gaussian Multi-Relay Networks and Asymptotic Capacity Results . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Multi-Relay Fading Channels . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193 193 194 197 201 203 203 207 213 214 216 219 225

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Cooperative Communications with Multiple Sources . . . . . . 6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA) . . 6.1.1 Round-Robin Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Opportunistic Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Code-Division Multiple Access (CDMA) . . . . . . . . . . . . . . . . . . . 6.2.1 Uplink CDMA with Designated Relays . . . . . . . . . . . . . . 6.2.2 Uplink CDMA with Shared Relays . . . . . . . . . . . . . . . . . 6.3 Space-Division Multiple Access (SDMA) . . . . . . . . . . . . . . . . . . . 6.4 Partner Selection Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Centralized Partner Selection Strategy . . . . . . . . . . . . . . 6.4.2 Decentralized Partner Selection Strategy . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

227 227 229 234 237 238 247 254 259 260 267 268

7

Cooperation Relaying in OFDM and MIMO Systems . . . . . 271 7.1 Brief Review of OFDM Systems . . . . . . . . . . . . . . . . . . . . . . . . . 271 7.2 Resource Allocation in Pair-Wise Cooperative OFDM Systems274 7.2.1 Power Allocation in Pair-Wise Cooperative OFDM Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 7.2.2 Subcarrier Matching for Pair-Wise Cooperative OFDM Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 7.3 Cooperative OFDM Systems with Multiple Relays . . . . . . . . . . 282 7.3.1 Cooperative Beamforming for OFDM Multi-Relay Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 7.3.2 Selective Relaying for OFDM Multi-Relay Systems . . . 288 7.4 Distributed Space-Frequency Codes . . . . . . . . . . . . . . . . . . . . . . . 292 7.4.1 Decode-and-Forward Space-Frequency Codes . . . . . . . . . 293 7.4.2 Amplify-and-Forward Space-Frequency Codes . . . . . . . . 301 7.5 Cooperation with MIMO Relays . . . . . . . . . . . . . . . . . . . . . . . . . . 305 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

8

Medium Access Control in Cooperative Networks . . . . . . . . 8.1 Cooperation with Slotted ALOHA . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Definition of Stability Region . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Stability Region of a Cooperative Pair . . . . . . . . . . . . . . 8.2 Collision Resolution Mechanisms in Cooperative Networks . . . 8.2.1 Network-Assisted Diversity Multiple Access (NDMA) . 8.2.2 Enhancements to NDMA with Relaying Users . . . . . . . . 8.3 Cooperation with CSMA/CA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Overview of the IEEE 802.11 MAC Protocol . . . . . . . . . 8.3.2 CoopMAC based on the IEEE 802.11 Protocol . . . . . . . 8.3.3 Analysis of CoopMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Automatic Retransmission reQuest (ARQ) with Cooperative Relays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Throughput Optimal Scheduling Protocols for Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

317 317 320 322 329 329 331 332 333 336 341 344 347

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8.5.1 Review of Throughput Optimal Control Policy for Non-Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . 347 8.5.2 Throughput Optimal Control Policy for Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 9

Networking and Cross-Layer Issues in Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 QoS in Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.1 QoS of a Simple Relay Network . . . . . . . . . . . . . . . . . . . . 9.1.2 QoS of a Cooperative Pair . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Routing in Cooperative Networks . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 General Formulation of Cooperative Routing . . . . . . . . . 9.2.2 Heuristic Algorithms for Cooperative Routing . . . . . . . . 9.3 Security Issues in Cooperative Networks . . . . . . . . . . . . . . . . . . . 9.3.1 Misbehavior in Relay Networks . . . . . . . . . . . . . . . . . . . . . 9.3.2 Security in Single-Relay Cooperative Networks . . . . . . . 9.3.3 Security in Multi-Relay Cooperative Networks . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

361 361 365 371 373 373 379 383 383 385 388 394

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Acronyms

3GPP ACK AF ARQ AWGN BC BER BS BPSK CDF CDMA CF CoMP CP CRC CS/CB CSI CSMA CSMA/CA CTS DCF DF DFE DFT DIFS DMC DMT DS-CDMA DSTC EC eNodeB

3rd Generation Partnership Project acknowledgement amplify-and-forward automatic retransmission request additive white Gaussian noise broadcast channel bit error rate base station binary phase-shift keying cumulative distribution function code-division multiple access compress-and-forward coordinated multiple point cyclic prefix cyclic redundancy check coordinated scheduling/beamforming channel state information carrier sensing multiple access CSMA with collision avoidance clear-to-send distributed coordination function decode-and-forward decision feedback equalizer discrete Fourier transform DCF interframe space discrete memoryless channel diversity-and-multiplexing tradeoff direct sequence code-division multiple access distributed space-time code equal-gain combining evolved node B

xvii

xviii

FDMA i.i.d. JP KKT LD LMMSE LOS LTE MAC MAI MF MGF MIMO MISO ML MMSE MRC MS MUD NACK NAV NLOS NP OFDM OSTBC PCF PDF QoS QPSK RS RSC RTS SC SDP SDMA SER SIFS SIMO SINR SISO SNR SS STBC STC STTC

Acronyms

frequency-division multiple access independent and identically distributed joint processing Karush-Kuhn-Tucker linear dispersion linear minimum mean square error line-of-sight Long-Term Evolution multiple access channel or medium access control multiple access interference matched filter moment generating function multiple-input multiple-output multiple-input single-output maximum likelihood minimum mean square error maximal ratio combining or maximal ratio combiner mobile station multi-user detection negative acknowledgement network allocation vector non-line-of-sight non-polynomial orthogonal frequency-division multiplexing orthogonal space-time block code point coordination function probability density function quality of service quadrature phase-shift keying relay station recursive systematic convolutional ready-to-send selection combining semi-definite programming space-division multiple access symbol error rate short interframe space single-input multiple-output signal-to-interference-plus-noise ratio single-input single-output signal-to-noise ratio subscriber station space-time block code space-time code space-time trellis code

Acronyms

xix

SVD TDMA UE VQ WiMAX ZF

singular value decomposition time-division multiple access user equipment vector quantizer or vector quantization Worldwide Interoperability for Mircowave Access zero-forcing

Chapter 1

Introduction

Wireless communications have gained much popularity in recent years due to its ability to provide untethered connectivity and mobile access. However, before the turn of the century, many attempts to achieve reliable and high data-rate communication over the wireless channel have been unsuccessful due to multipath fading, shadowing, and path loss effects. These effects result in random variations of channel quality in time, frequency, and space, making it difficult to employ conventional wireline communication techniques in the wireless environment. Not until the past two decades have people developed effective transmit and receive diversity techniques to exploit diversity in different channel dimensions, such as time, frequency, and space, and achieve the so-called diversity gains. With sufficient channel knowledge, one can devise power and bit allocation policies over time, frequency, and space that allocates more resources to channels that are more reliable and avoids transmitting in bad channels to preserve energy. Even when channel knowledge is not available, space-time or space-frequency codes can also be utilized to enhance the transmission reliability. In particular, advances in theory on multiple-input multiple-output (MIMO) [17, 47, 53] systems have made it desirable to embed multiple antennas on modern wireless transceivers, in order to achieve spatial diversity gains. However, as the size and cost of wireless devices are limited for many applications, e.g., in sensor networks or for cellular phones, placing multiple antennas on a single terminal may not be practical. In this case, cooperating with other nodes in the network to form a distributed antenna system becomes a desirable and promising alternative. This is achieved by the so-called cooperative communications.

1.1 An Overview on Cooperative Communications Cooperative communications, as popularized by the works [36, 37, 41, 42], allow users in the system to cooperate by relaying each other’s messages to the

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_1, © Springer Science+Business Media, LLC 2010

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2

1 Introduction SNR

User 1 xxxxxx xxxxxx xxxxxx

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xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx

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t

Fig. 1.1 Illustration of a pair-wise cooperative communication system.

destination. By doing so, users can effectively form a distributed antenna array that emulates the spatial diversity gains achievable by centralized MIMO systems. An example of a pair-wise cooperative communication system is illustrated in Fig. 1.1, where the users are assumed to experience independent fading channels to the destination. Due to multipath fading, the signal-tonoise ratios (SNRs) at the destination may vary rapidly over time, causing communication outage whenever one of the users’ SNRs falls below the required level (as illustrated by the shaded regions in Fig. 1.1). However, if the two users can cooperate by relaying each others’ messages to the destination, communication outage will occur only when both users simultaneously experience poor channels, thereby improving the transmission reliability. Many cooperation strategies have been proposed in the literature based on different relaying techniques, such as amplify-and-forward (AF) [36], decodeand-forward (DF) [36,42], selective relaying (SR) [36], coded cooperation [27], compress-and-forward (CF) [34] etc. When these schemes are employed in a pair-wise cooperating system as shown in Fig. 1.1, we can assume that, at each instant in time, only one user acts as the source while the other user serves as the relay that forwards the source’s message to the destination. The role between the source and the relay can be interchanged at any instant in time. An illustration of the DF, AF, and SR schemes are given in Fig. 1.2. As common in these schemes, cooperative transmissions are initiated by first having the source (e.g., user 1) broadcast its message to both the relay and the destination. If the DF scheme is employed, the relay will decode and regenerate a new message to the destination in the subsequent time slot. At the destination, signals from both the source and the relay are combined to provide better detection performance. As an extension to the DF scheme, the message generated by the relay can be re-encoded to provide additional error protection, and such a scheme can also be referred to as coded cooperation. If the AF scheme is employed, the relay simply amplifies the received signal and forwards it directly to the destination without explicitly decoding the message. The SR scheme, on the other hand, is a dynamic scheme where relays are selected to retransmit the source message only if the relay path

1.1 An Overview on Cooperative Communications

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Selective Relaying (SR) Method

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(c) Selective Relaying Fig. 1.2 Examples of basic cooperative communication schemes, namely, decode-andforward, amplify-and-forward, and selective relaying.

is sufficiently reliable. This scheme can be applied on top of both DF and AF schemes to improve cooperation efficiency. Among the many cooperation schemes proposed in the literature, DF, AF, and SR schemes are the most basic and widely adopted. More sophisticated schemes, such as the CF scheme, can also be devised by exploiting the statistical dependencies between the messages received at the relay and the destination but require higher implementation complexity. These schemes will be described in Chapter 3. Most cooperation strategies involve two phases of transmission: the coordination phase and the cooperative transmission phase. Coordination is especially required in cooperative systems since the antennas are distributed among different terminals, as opposed to that in centralized MIMO systems. Although extra coordination may reduce bandwidth inefficiency, the cost is often compensated for by the large diversity gains experienced at high SNR. Specifically, coordination can be achieved either by direct inter-user communication or by the use of feedback from the destination. Based on the information obtained through coordination, cooperating partners will compute and transmit messages so as to reduce the transmission cost or enhance the detection performance at the receiver. The cooperative communication schemes described above can be readily extended to a large network by having one user serve as the source and the remaining users serve as relays at each time instant, as shown in Fig. 1.3. The relays together form a distributed antenna array that is able to achieve

4

1 Introduction

u1=f1(x1) Relay 1 Relay 2

yL

.....

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.....

x

y2

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Fig. 1.3 Illustration of multi-relay cooperative communication system.

spatial diversity and multiplexing gains [5,57,59] similar to that of centralized MIMO systems. These techniques include distributed space-time coding [3,29, 30,43,56], distributed beamforming [2,31,33], and antenna selection strategies [7,16,58]. However, the cost of coordination may increase with the number of cooperating users and, thus, efficient inter-user or feedback communication strategies must be devised to make cooperation worthwhile. In the early literature on cooperative communications, most works consider the simplified scenario where only one user in the system acts as the source while all the other users serve as relays to the source. When applying these schemes to systems with multiple sources, as illustrated in Fig. 1.4, one must assume that the sources can access the relays through orthogonal channels and that the relays have sufficient energy and bandwidth resources for all users. If this is not the case, many multiuser problems may arise in both the physical and higher network layers. From the physical layer perspective, this results in multiple access interference (MAI) that may eventually dominate the BER performance and, thus, cause the diversity gains to diminish. Moreover, with limited energy and bandwidth resources at the relays, efficient resource allocation policies must be devised to ensure high performance gains for all users. From the medium access control (MAC) layer perspective, scheduling or random access protocols must be developed to help resolve contention among those competing for the cooperative channel. In fact, even when cooperative advantages do not exist in the physical layer, it has been shown in [23] that MAC layer cooperation can still be beneficial. For relay and cooperative communication technology to be fully deployed in modern communication systems, many high-layer issues remain to be addressed. The most evident among all is the issue of resource allocation between the source-relay, relay-destination, and source-destination links. This

1.2 Brief History of Cooperative and Relay Channels

5

r1

r2

s1

..

s2

..

..

..

..

rL

sK-1 sK Fig. 1.4 Illustration of cooperative systems with multiple sources.

can be particularly difficult in a cellular system that involves multiple relay stations, each serving a different subset of users. Moreover, user mobility is also a major issue as cooperative links may not be stable if cooperating users/relays are mobile and handoff between relay stations may be frequent. Other issues such as buffer management and cooperative queuing must be addressed to determine how the relay packets should be treated at each relay or cooperative user; security and authentication mechanisms must be developed to combat the effect of malicious or unauthorized relays. In multihop networks, finding the optimal cooperative route is known to be NP-hard and, thus, efficient cooperative routing protocols must be designed. Some of these issues will also be addressed in this book (c.f. Chapters 8 and 9), but research in these areas are still in the preliminary stage.

1.2 Brief History of Cooperative and Relay Channels The main idea behind cooperative communications stems from the study of relay channels, initially proposed by van der Meulen in [49, 50]. In these works, general three terminal communication problems have been formulated and bounds have been provided for the capacity of relay channels (i.e., the channel that consists of a source transmitting to a destination with the help of a relay). Later in 1979, Cover and El Gammal published further results on relay channels in [10], where significantly improved inner and outer bounds were derived. This is considered the most prominent work on relay channels up till this date since many of the results still could not be superseded. The studies on relay channels has continued into the 1980s where efforts to extend

6

1 Introduction

the results of [10] to multiple relays have been made in [4,14] and the capacity of semideterministic relay channels have been derived in [15]. However, since the early 1980s, the interest in this area has decreased considerably, possibly due to theoretical difficulties as well as practical challenges in implementation. In fact, after several decades, the fundamental performance limits (e.g., the capacity) of the relay channel still remain to be unknown. Interestingly, during this time, important progress has been made on related problems, such as broadcast channels (BC) [6, 11, 20, 21, 39], multiple access channels (MAC) [12,13,18], and multiterminal source coding [44,54]. In relay channels, one can view the transmission from the source to the relay and the destination as a BC while the transmission from the source and the relay to the destination can be viewed as a MAC. Moreover, since the data transmitted by the relay is correlated with that at the source, the relay channel problem is also intimately related with multiterminal source coding problems. Hence, the results obtained in these areas have contributed substantially to the understanding of relay channels today. In particular, the works on MAC with generalized feedback, e.g., [9, 32], are said to include relaying and cooperative communication as special cases since information at the relay can be considered as information provided through feedback. These works have set a solid foundation for more recent works in these fields, such as [8, 28, 48, 51], and has led to further advances in relay channels, e.g., [24, 35, 52, 55]. In earlier years, research on relay channels have appeared mostly in the information theory community, focusing on the fundamental limits of the channel. However, with the advances in MIMO [17, 45–47] and coding techniques [19, 38] and due to the increased feasibility of practical implementation, interest in the communication and signal processing community has grown exponentially in recent years. Specifically, the exploding interest in cooperative communications was initiated by the works of Sendonaris et al in [42] and of Laneman et al in [36, 37], and also, from the signal processing community, of the works by Scaglione and Hong in [22, 41]. Since then, relay and cooperative communication has quickly been identified as an effective scheme to achieve spatial diversity and to extend the coverage of high data rates. Numerous relay and cooperative transmission schemes have been rapidly developed in the communication and signal processing community and studies in the high-layer issues, such as medium access control, routing, or quality-of-service, have been identified by the networking community.

1.3 Standardization of Cooperative Communication and Relay Technology Cooperative communication and relay technologies have gradually made its way towards wireless standards, such as IEEE 802.16j and Long Term Evolution (LTE)-Advanced. Specifically, IEEE 802.16j [26], an amendment to IEEE

1.3 Standardization of Cooperative Communication and Relay Technology

7

802.16e mobile Worldwide Interoperability for Microwave Access (WiMAX) standard [25], supports relay functionalities that can be used to increase the throughput of cell-edge users and extend system coverage to the interior of buildings, to temporary locations, and also to within mobile transportation vehicles [40]. Relay stations (RSs) can also forward messages to other RSs further away from the base station (BS) to form a multihop relay network. Depending on whether or not mobile stations (MSs) are aware of the relays’ existence, relays in the IEEE 802.16j standard can be categorized as transparent and nontransparent relays. Transparent relays are used when subscriber stations (SSs) are able to receive control information directly from the BS. Thus, the RSs are used purely to enhance throughput of the SSs without transmitting additional control information. On the other hand, nontransparent relays can be used to serve MSs that are located outside or near the edge of the BS coverage by serving as a virtual BS to the MSs. In this case, the RS must be able to transmit control information and synchronization messages to the MS. With transparent relays, bandwidth allocation must be achieved through centralized scheduling while, with nontransparent relays, this can be done distributively at the RSs. Moreover, transparent relays must communicate with the BS and the MS using the same frequency band while nontransparent relays may utilize different frequency bands on the two links. Although more flexibility is allowed with nontransparent relays, increased implementation complexity is also required. Furthermore, cooperative relaying functionalities that enable the BS and the RSs to transmit cooperatively to the MS have also been defined in IEEE 802.16j. In particular, three types of cooperative transmission schemes have been specified, namely, the cooperative source diversity, the cooperative transmit diversity, and the cooperative hybrid diversity techniques. In the cooperative source diversity scheme, the cooperating transmitters will simultaneously transmit the same signal to the MS using the same time-frequency channel to enhance the SNR at the MS. In the cooperative transmit diversity scheme, transmitters will together form a distributed antenna array where space-time codes (previously defined in 16e [25]) can be used across the distributed antennas. The cooperative hybrid diversity scheme, which is a combination of the above two schemes, also employs space-time codes across the distributed RSs (or the BS), but two or more antennas may be transmitting identical signals in this case. In addition to IEEE 802.16j, relaying and cooperative transmission technologies are also being incorporated into the recent LTE-Advanced standard [1] specified by the 3rd Generation Partnership Project (3GPP). Here, the goal is also to extend coverage of high-data rates and improve group mobility, facilitate temporary network deployment, and increase cell-edge throughput. The relays in LTE-Advanced can be classified into inband or outband relays depending on whether or not the communication to the base station (or called eNodeB) and to the end user (or called User Equipment, UE) are over the same frequency band. The relays can also be classified as

8

1 Introduction

transparent or nontransparent depending on whether or not the UEs are aware of their existence, similar to IEEE 802.16j. Moreover, depending on the relay strategy, relays can also be part of a donor cell or control cells of its own. Although the LTE-Advanced standard has not yet been finalized, there is consensus that at least the Type 1 relay will be incorporated into the system. A Type 1 relay is an inband relay node that controls its own cell and, thus, sends its own synchronization signals, reference symbols, scheduling information etc. This type of relay consists of its own Physical Cell ID and appears to the UE as a separate base station. In any case, by connecting relays wirelessly to the eNodeB, backhaul wireline connections to the radio access network are no longer necessary and the cost of coverage extension is significantly reduced. Besides the relay functionality, LTE-Advanced also may incorporate Coordinated Multiple Point (CoMP) transmission and reception [1], where the eNodeBs cooperate in communicating to and from the UE, to improve coverage of high data rates and cell-edge throughput. Specifically, on the downlink, coordinated transmission strategies can be classified into Joint Processing (JP) and Coordinated Scheduling/Beamforming (CS/CB). With JP, the data destined for the UE is available at each point in the CoMP cooperating set (i.e., eNodeBs that participate in the coordinated transmission). Two types of JP transmission can be employed, namely, the Joint Transmission scheme, where data is transmitted simultaneously from multiple points of the cooperating set, e.g., to improve the received signal quality and/or cancel active interference from other data transmission, and the Dynamic Cell Selection scheme, where data is transmitted from one point at a time. The former scheme can be based on, e.g., distributed beamforming or space-time coding, while the latter scheme is based on antenna selection (c.f. Chapter 4). With CS/CB, data is only available at the serving cell, but scheduling or beamforming decisions can be made in coordination with other cells to avoid interference. Although CoMP does not involve relaying, cooperation is achieved by employing similar strategies as those in Chapters 4 and 6, except that the transmitting terminals are linked through wireline connections. Overall, regardless of the specific relay and/or cooperation strategy that will be employed in IEEE 802.16j and LTE-Advanced, these examples demonstrate the importance of relay and cooperative communication technology in next generation wireless networks.

1.4 Book Outline The aim of this book is to serve as the main reference for a graduate course on cooperative communications and also for engineers working in this area. While cooperative communication technology has evolved tremendously over

1.4 Book Outline

9

the years, we focus on describing the basic techniques that have been proposed and hopefully prepare readers for further studies in the field. In Chapter 2, an introduction to wireless communications is provided, including brief reviews of fading channels, diversity techniques, channel capacity, and the concept of diversity-multiplexing tradeoff (DMT). In Chapter 3, basic cooperative communication techniques for two-user systems are described. In these schemes, one user serves as the source while the other user serves as the relay that helps forward the source’s messages to the destination. Basic techniques such as decode-and-forward (DF), amplifyand-forward (AF), and coded cooperation (CC) schemes will be described in detail. The concept of compress-and-forward (CF) will be briefly discussed while readers will be referred to Chapter 5 for more details. Channel estimation schemes for the single relay scenario will also be described. In Chapter 4, cooperative transmission schemes that involve multiple relays is described. Similar to the case with a single relay, we assume that, at each instant in time, only one user serves as the source while the other users can be viewed as a distributed antenna array that relays the sources message cooperatively to the destination. The DF and AF schemes can be extended to the multi-relay scenario by assuming the availability of orthogonal channels at each relay. When orthogonal channels do not exist, the distributed antenna array formed by the relays can be used to emulate many MIMO techniques subject to individual constraints at the relays or limitations due to lack of coordination. Depending on the level of channel state information (CSI) available at the source and relays, different schemes such as transmit beamforming, selective relaying, or distributed space-time coding schemes can be employed. Channel estimation schemes for the case of multiple relays are also discussed. In addition, asynchronous cooperation schemes are also introduced to show how cooperation can still be helpful without full coordination among the relays. Furthermore, in the case where some relays are located beyond the transmission range of the source, multihop relaying schemes will also be discussed to demonstrate how cooperative transmissions can effectively extend the coverage of wireless transmission over a wide area. In Chapter 5, information-theoretic aspects of the relay channel are introduced. The basic results on the relay channel capacity given by Cover and El Gamal will be reviewed and, then, some results on large relay networks will be given. Since one of the advantages of cooperative communications is to emulate the spatial diversity and multiplexing gains provided by conventional MIMO systems, we also discuss some results on the diversity-multiplexing tradeoff (DMT) of cooperative relay networks. In Chapter 6, a cooperative relay system with multiple sources competing for the use of the cooperative channel is discussed. We discuss the cases where cooperative users operate under time-division multiple access (TDMA), frequency-division multiple access (FDMA), code-division multiple access (CDMA), and space-division multiple access (SDMA) schemes. In these cases, resource allocation among the different users and between sources

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1 Introduction

and relays are important to ensure high performance for all users. Moreover, in the case of CDMA and SDMA, effective mitigation of multiple access interference is critical to preserve the spatial diversity gains obtained through cooperation. Finally, partner selection algorithms will also be introduced to show how users can efficiently select cooperating partners. In Chapter 7, the use of cooperative relaying on top of advanced communication systems such as OFDM and MIMO systems is discussed. In the case of multicarrier systems, frequency diversity can be exploited in addition to the spatial diversity gains by allowing users to cooperatively allocate their power across subcarriers. When perfect CSI is not available, space-frequency codes are introduced to exploit the diversity gains. Moreover, when multiple antennas are available at each terminal, further spatial diversity gains can be exploited in addition to the cooperative gains. The difference compared to single-antenna cooperative systems is that each terminal now has full control of a subset of antennas and, thus, lesser restrictions are imposed on the transmission schemes that can be developed. In Chapters 8 and 9, MAC, networking, and cross-layer issues regarding cooperative communications are discussed. Specifically, in Chapter 8, we show how cooperation in the physical layer may affect MAC layer designs and what cooperative advantages can be exploited in the MAC layer. These issues are addressed with studies on the fundamental slotted ALOHA protocol, the CSMA/CA protocol, and centralized scheduling schemes. In Chapter 9, we address networking or cross-layer issues such as quality-of-service (QoS) considerations, cooperative routing for multihop networks, and security issues. It is shown that existing MAC or network layer protocols must be modified or redesigned to fully exploit the advantages of cooperation in the physical layer and to explore further advantages in the MAC layer and above.

References 1. 3rd General Partnership Project: Technical specification group radio access network: Further advancements for E-UTRA physical layer aspects (Release 9). Tech. Rep. 36.814 (V9.0.0) (2010) 2. Abdallah, M.M., Papadopoulos, H.C.: Beamforming algorithms for information relaying in wireless sensor networks. IEEE Transactions on Signal Processing 56(10), 4772–4784 (2008) 3. Anghel, P., Leus, G., Kaveh, M.: Distributed space-time cooperative systems with regenerative relays. IEEE Transactions on Wireless Communications 5(11), 3130–3141 (2006) 4. Aref, M.R.: Information flow in relay networks. Ph.D. thesis, Stanford University (1980) 5. Azarian, K., El Gamal, H., Schniter, P.: On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels. IEEE Transactions on Information Theory 51(12), 4152–4172 (2005) 6. Bergmans, P.P., Cover, T.M.: Cooperative broadcasting. IEEE Transactions on Information Theory 20(3), 317–324 (1974)

References

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7. Bletsas, A., Khisti, A., Reed, D.P., Lippman, A.: A simple cooperative diversity method based on network path selection. IEEE Journal on Selected Areas in Communications 24(3), 659–672 (2006) 8. Caire, G., Shamai, S.: On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Transactions on Information Theory 49(7), 1691–1706 (2003) 9. Carleial, A.B.: Multiple-access channels with different generalized feedback signals. IEEE Transactions on Information Theory 28(6), 841–850 (1982) 10. Cover, T., El Gamal, A.: Capacity theorems for the relay channel. IEEE Transactions on Information Theory 25(5), 572–584 (1979) 11. Cover, T.M.: Broadcast channels. IEEE Transactions on Information Theory 18(1), 2–14 (1972) 12. Cover, T.M., El Gamal, A., Salehi, M.: Multiple access channels with arbitrarily correlated sources. IEEE Transactions on Information Theory 26(6), 648–657 (1980) 13. Cover, T.M., Leung, C.S.K.: An achievable rate region for the multiple-access channel with feedback. IEEE Transactions on Information Theory 27(3), 292–298 (1981) 14. El Gamal, A.: On information flow in relay networks. In: Proceedings of IEEE National Telecommunications Conference, vol. 2, pp. D4.1.1–D4.1.4. Miami, FL (1981) 15. El Gamal, A., Aref, M.: The capacity of the semideterministic relay channel. IEEE Transactions on Information Theory IT-28(3), 536 (1982) 16. Fareed, M.M., Uysal, M.: On relay selection for decode-and-forward relaying. IEEE Transactions on Wireless Communications 8(7), 3341–3346 (2009) 17. Foschini, G.J.: Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell System Technical Journal 1, 41–59 (1996) 18. Gaarder, N., Wolf, J.: The capacity region of a multiple-access discrete memoryless channel can increase with feedback. IEEE Transactions on Information Theory 21(1), 100–102 (1975) 19. Gallager, R.G.: Low density parity check codes. Ph.D. thesis, Massachusetts Institute of Technology (1963) 20. Gel’fand, S.I., Pinsker, M.S.: Capacity of a broadcast channel with one deterministic component. Problemy Peredachi Informatsii 16(1), 24–34 (1980) 21. Han, T.S., Costa, M.H.M.: Broadcast channels with arbitrarily correlated sources. IEEE Transactions on Information Theory 33(5), 641–650 (1987) 22. Hong, Y.-W., Scaglione, A.: Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks. IEEE Transactions on Wireless Communications 5(10), 2844–2855 (2006) 23. Hong, Y.-W. P., Lin, C.-K., Wang, S.-H.: Exploiting cooperative advantages in slotted ALOHA random access networks. to appear in IEEE Transactions on Information Theory (2010) 24. Host-Madsen, A., Zhang, J.: Capacity bounds and power allocation for wireless relay channels. IEEE Transactions on Information Theory 51(6), 2020–2040 (2005) 25. IEEE Standard 802.16e-2005: IEEE Standard for Local and Metropolitan Area Networks - Part 16: Amendment for Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands (2005) 26. IEEE Standard 802.16j-2009: IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air interface for broadband wireless access systems – Multihop relay specification (2009) 27. Janani, M., Hedayat, A., Hunter, T.E., Nosratinia, A.: Coded cooperation in wireless communications: Space-time transmission and iterative decoding. IEEE Transactions on Signal Processing 52(2), 362–371 (2004) 28. Jindal, N., Vishwanath, S., Goldsmith, A.: On the duality of Gaussian multiple-access and broadcast channels. IEEE Transactions on Information Theory 50(5), 768–783 (2004) 29. Jing, Y., Hassibi, B.: Distributed space-time coding in wireless relay networks. IEEE Transactions on Wireless Communications 5(12), 3524–3536 (2006)

12

1 Introduction

30. Jing, Y., Jafarkhani, H.: Distributed differential space-time coding for wireless relay networks. IEEE Transactions on Communications 56(7), 1092–1100 (2008) 31. Jing, Y., Jafarkhani, H.: Network beamforming using relays with perfect channel information. IEEE Transactions on Information Theory 55(6), 2499–2517 (2009) 32. King, R.C.: Multiple access channels with generalized feedback. Ph.D. thesis, Stanford University (1978) 33. Koyuncu, E., Jing, Y., Jafarkhani, H.: Distributed beamforming in wireless relay networks with quantized feedback. IEEE Journal on Selected Areas in Communications 26(8), 1429–1439 (2008) 34. Kramer, G., Gastpar, M., Gupta, P.: Capacity theorems for wireless relay channels. In: Proc. 41st Annu. Allerton Conf. Communications, Control, and Computing, pp. 1074–1083. Monticello, IL (2003) 35. Kramer, G., Gastpar, M., Gupta, P.: Cooperative strategies and capacity theorems for relay networks. IEEE Transactions on Information Theory 51(9), 3037–3063 (2005) 36. Laneman, J.N., Tse, D.N.C., Wornell, G.W.: Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Transactions on Information Theory 50(12), 3062–3080 (2004) 37. Laneman, J.N., Wornell, G.W.: Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks. IEEE Transactions on Information Theory 49(10), 2415–2425 (2003) 38. MacKay, D.J.C.: Good error-correcting codes based on very sparse matrices. IEEE Transactions on Information Theory 45(2), 399–432 (1999) 39. Marton, K.: A coding theorem for the discrete memoryless broadcast channel. IEEE Transactions on Information Theory 25(3), 306–311 (1979) 40. Peters, S.W., Heath, Jr., R.W.: The future of WiMAX: Multihop relaying with IEEE 802.16j. IEEE Communications Magazine pp. 104–111 (2009) 41. Scaglione, A., Hong, Y.-W.: Opportunistic large arrays: Cooperative transmission in wireless multihop ad hoc networks to reach far distances. IEEE Transactions on Signal Processing 51(8), 2082–2092 (2003) 42. Sendonaris, A., Erkip, E., Aazhang, B.: User cooperation diversity–Part I: System description” and “User cooperation diversity–Part II: implementation aspects and performance analysis. IEEE Transactions on Communications 51(11) 1927–1938 and 1939–1948 (2003) 43. Sirkeci-Mergen, B., Scaglione, A.: Randomized space-time coding for distributed cooperative communication. IEEE Transactions on Signal Processing 55(10), 5003–5017 (2007) 44. Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4), 471–480 (1973) 45. Tarokh, V., Jafarkhani, H., Calderbank, A.: Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory 45(5), 1456–1467 (1999) 46. Tarokh, V., Seshadri, N., Calderbank, A.: Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Transactions on Information Theory 44(2), 744–765 (1998) ˙ 47. Telatar, I.E.: Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications 10(6), 585–595 (1999) 48. Tse, D.N.C., Viswanath, P., Zheng, L.: Diversity-multiplexing tradeoff in multipleaccess channels. IEEE Transactions on Information Theory 50(9), 1859–1874 (2004) 49. van der Meulen, E.C.: Transmission of information in a T-terminal discrete memoryless channel. Ph.D. thesis, Department of Statistics, University of California, Berkeley, CA (1968) 50. van der Meulen, E.C.: Three-terminal communication channels. Advances in Applied Probability 3(1), 120–154 (1971) 51. Vishwanath, S., Jindal, N., Goldsmith, A.: Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Transactions on Information Theory 49(10), 2658–2668 (2003)

References

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52. Wang, B., Zhang, J., Host-Madsen, A.: On the capacity of MIMO relay channels. IEEE Transactions on Information Theory 51(1), 29–43 (2005) 53. Winters, J.H.: On the capacity of radio-communication systems with diversity in a Rayleigh fading environment. IEEE Journal on Selected Areas in Communications 5(5), 871–878 (1987) 54. Wyner, A., Ziv, J.: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 22(1), 1–10 (1976) 55. Xie, L.-L., Kumar, P.R.: An achievable rate for the multiple-level relay channel. IEEE Transactions on Information Theory 51(4), 1348–1358 (2005) 56. Yiu, S., Schober, R., Lampe, L.: Distributed space-time block coding. IEEE Transactions on Communications 54(7), 1195–1206 (2006) 57. Yuksel, M., Erkip, E.: Multiple-antenna cooperative wireless systems: a diversity- multiplexing tradeoff perspective. IEEE Transactions on Information Theory 53(10), 3371–3393 (2007) 58. Zhao, Y., Adve, R., Lim, T.: Improving amplify-and-forward relay networks: optimal power allocation versus selection. In: Proceedings on the IEEE International Symposium on Information Theory (ISIT), pp. 1234–1238 (2006) 59. Zheng, L., Tse, D.N.C.: Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory 49(5), 1073– 1096 (2003)

Chapter 2

Review of Wireless Communications and MIMO Techniques

To facilitate the understanding of the cooperative strategies introduced throughout this book, we provide in this chapter a brief review of wireless communications and MIMO techniques. First, we introduce some basic characteristics of the wireless environment, including path loss, shadowing, and multipath fading, and describe popular diversity techniques that are often used to combat these channel effects. Then, we review the fundamental limits of Gaussian AWGN channels, fading channels, and multiple-input multiple-output (MIMO) channels. Finally, we conclude with a discussion on the diversity-and-multiplexing tradeoff (DMT) for MIMO systems. More detailed discussions on these topics can be found in [11] and [37].

2.1 Characteristics of Wireless Channels The development of wireless communications have progressed tremendously over the past few decades due to advances in wireless hardware technology and also because of the large demand for mobile access. Compared with conventional wireline communications, the signal transmitted over wireless channels may suffer dramatic attenuation, large delays, and severe signal distortion. These non-ideal effects are often characterized by three factors, i.e., path loss, shadowing, and multi-path fading. In this section, we will briefly describe what these factors are and their effect on the performance of wireless communication systems.

2.1.1 Path Loss Consider a point-to-point wireless communication system where a transmitter communicates with a receiver by sending an electromagnetic signal through

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_2, © Springer Science+Business Media, LLC 2010

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the wireless medium. The strength of the signal attenuates as it traverses the medium and, thus, becomes weaker as the propagation distance increases. The amount of degradation in the signal strength with respect to the distance can be characterized by the ratio between the transmit power, Pt , and the receive power, Pr , which is denoted by PL =

Pt . Pr

This ratio is used to quantify the effect of path loss and the value may depend on the geographic environment as well as certain radio properties, such as the spaciousness of the environment, the transmission distance, the radio wavelength, heights of the transmitter and receiver, ... etc. The path loss is usually represented in the decibel scale, i.e., PL(dB) = 10 log10

Pt . Pr

(2.1)

Several path loss models are given in the following.

Free Space Propagation When the radio propagates in free space, a line-of-sight (LOS) path may exist between the transmitter and the receiver, while the signal reflections may be negligible. In this case, the strength of the received signal can be expressed as [21] 2  λc Pr = Pt Gt Gr , (2.2) 4πd where d is the geographic distance between the transmitter and the receiver, Gt and Gr are the antenna gains at the transmitter and receiver, respectively, and λc is the wavelength of the electromagnetic signal. Here, the power of the received signal decays with the square of the transmission distance d.

Two-Ray Model In addition to free space propagation, we can also consider a slightly more realistic model that consists of one LOS path and one path obtained through ground reflection, as shown in Fig. 2.1. Since two propagation paths are considered, the model is often referred to as the two-ray model. Let us denote by d the distance between the transmitter and the receiver, and denote by ht and hr the effective heights of the transmit and receive antennas, respectively. Moreover, as shown in Fig. 2.1, we can define dLOS as the length of the LOS path, dG as the distance between the transmit antenna and the reflecting point, and dG as the distance between the reflecting point and the receive

2.1 Characteristics of Wireless Channels

17

Fig. 2.1 Two-ray propagation model.

antenna. Assume that the distance d is much greater than the heights of the transmit and receive antennas (i.e., d  ht , hr ) so that d ≈ dLOS ≈ dG + dG and so that the received signal power can be approximated as [23, 27]  Pr ≈

ht hr d2

2 Gt Gr Pt .

(2.3)

Due to the interference between the LOS and the reflection paths, the received signal power decays with the fourth power of the distance d when the distance between the transmitter and the receiver is sufficiently large.

A General Path-Loss Model To facilitate analytical studies on wireless communication systems, it is often convenient to use simpler path loss models, with the help of experimental data to characterize key parameters. Specifically, a simple path loss model often adopted in practice is the following:  Pr = Pt K

d d0

−α ,

(2.4)

where d0 is the reference distance, K is a constant related to the antenna gain and the average channel attenuation, and α is the path-loss exponent. The constant K can be obtained from the empirical average of the receive power at the reference distance d0 . Due to the scattering phenomena in the antenna near field, the simplified model in (2.4) is valid only when the distance d is greater than the reference distance d0 . The reference distance is usually 1-10 meters indoors and 10-100 meters outdoors [11]. The value of the path loss exponent α depends on the propagation environment and usually ranges between 2 and 6. The actual value of the path-loss exponent is obtained by

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2 Review of Wireless Communications and MIMO Techniques

fitting the prescribed model to the empirical data. The parameters for several typical environments are listed as follows [9, 11, 20, 23, 30, 35, 36]. • • • • • • • •

Free space: α = 2 Urban macrocells: 3.7 ≤ α ≤ 6.5 Urban microcells: 2.7 ≤ α ≤ 3.5 Office building (same floor): 1.6 ≤ α ≤ 3.5 Office building (multiple floors): 2 ≤ α ≤ 6 Store: 1.8 ≤ α ≤ 2.2 Factory: 1.6 ≤ α ≤ 3.3 Home: α ≈ 3

The simplified path loss model can be expressed in decibel scale by   d Pr(dBm) = Pt(dBm) + K(dB) − 10α log10 . d0

(2.5)

More details of the modeling and of the effect of the path loss phenomena in wireless communications can be found in [11, 23].

2.1.2 Shadowing Effect In addition to the power loss caused by free-space attenuation, the radio waves may also be distorted by the obstacles that appear along the transmission paths. These obstacles may absorb part of the signal energy, resulting in signal strength degradation or cause random scattering. The effects may vary slowly over time due to the relative motion between the transmitter, the receiver, and near-by obstacles along the propagation path, such as buildings, trees, vehicles, or airplanes. This slow-varying power variation is called the shadowing effect and is considered as a type of large-scale (or macroscale) fading. Based on the experimental measurements, the variation on the receive signal strength can be modeled as a log-normal random variable with probability density function (PDF) given by [2, 7]   (10 log10 ψ − μψdB )2 ξ fψ (ψ) = √ , ψ > 0, (2.6) exp − 2σψ2 dB 2πσψdB ψ where ξ = 10/ ln(10) is a constant. The received signal strength in decibel, i.e., ψdB  10 log10 ψ, is a Gaussian random variable with mean μψdB and variance σψ2 dB . To jointly consider both the path loss and shadowing effects, we may simply combine the log-normal distributed shadowing effect with the average path loss as   Pr d − ψdB . = 10 log10 K − 10α log10 (2.7) Pt (dB) d0

2.1 Characteristics of Wireless Channels

19

Power loss: Pr/Pt (dB)

Path Loss Shadowing Effect Multipath Fading

Distance between Tx and Rx: log(d/d0) Fig. 2.2 Illustration of path loss, shadowing effect and multipath fading.

2.1.3 Multipath Fading In wireless communication systems, signals are transmitted through environments full of reflectors or scatters and, thus, the signals eventually arriving at the receiver are often superpositions of signals from multiple propagation paths. The signals arriving from different paths may add up either constructively or destructively at the receiver and, thus, the signal strength may fluctuate rapidly over time, space, and frequency. This is the so-called multipath fading effect, which results in fast and small-scale (or microscale) amplitude and phase distortion. The various effects of path loss, shadowing, and multipath fading on the received signal strength is illustrated in Fig. 2.2. We can see that the long-term average receive power decreases with the propagation distance while large-scale and small-scale perturbation on the receive power also exist due to shadowing and multipath fading, respectively.

Continuous-Time Passband Model In general, fading channels in wireless systems can be modeled as linear timevarying systems. Let us consider a rich scattering environment where the received signal can be modeled as a superposition of signals received from

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different reflecting or scattering path. We assume that the transmitter, the receiver and/or the reflectors and scatters in the environment are in motion and, thus, the number of propagation paths, the signal propagation distances, and the channel gains may all vary with time. Let x(t) be the signal transmitted by the transmitter. The time-varying signal observed by the receiver can be expressed as 

N (t)

y(t) =

ai (t)x(t − τi (t)),

(2.8)

i=1

where N (t) is the number of propagation paths for the transmitted signal at time t, and ai (t) and τi (t) are the attenuation and the propagation delay of the i-th path at time t. Here, the received signal can be expressed as the output of a linear time-varying system where  ∞ y(t) = h(τ, t)x(t − τ )dτ. (2.9) −∞

The channel impulse response is given by 

N (t)

h(τ, t) =

ai (t)δ(τ − τi (t)),

(2.10)

i=1

and the channel frequency response is given by 

N (t)

H(f, t) =

ai (t)e−j2πf τi (t) .

(2.11)

i=1

The attenuation and delay depend on the propagation distance of each path.

Continuous-Time Baseband Equivalent Model In practical wireless systems, the radio spectrum is typically divided into parallel sub-bands and each transmitter’s signal must be limited to a certain frequency band, say [fc − W/2, fc + W/2], where fc is the center frequency and W is called the signal bandwidth. For example, the Global System for Mobile Communications (GSM) standard uses 890 − 915 MHz band for the cellular uplink (i.e., mobile to base station) but allocates bandwidth of only W = 200 KHz to each mobile’s transmissions. The signals are “up-converted” to the center frequency fc before transmission and is “down-converted” back to baseband (fc = 0) at the receiver in order to perform further processing. Since the up-conversion and down-conversion of signals do not affect the baseband signal processing conducted at the transceivers, the cooperative signal processing techniques discussed in later chapters will mostly be introduced

2.1 Characteristics of Wireless Channels

21

Fig. 2.3 Illustrations of the power spectral density of a bandpass signal and its corresponding complex baseband equivalent signal.

using equivalent baseband signal models. The conversion from a continuoustime passband signal model to a continuous-time baseband equivalent model can be described as follows. Let x(t) be a narrowband bandpass signal with bandwidth W and center frequency fc , which can be expressed as √  x(t) = 2Re xb (t)ej2πfc t , (2.12) where xb (t) is the complex baseband equivalent signal of x(t). The baseband equivalent signal xb (t) is obtained by down-converting the positive portion of the spectrum of x(t) to the baseband. Therefore, in the frequency domain, the power spectral density (PSD) of xb (t) can be expressed as

√ 2S(f + fc ), f + fc ≥ 0, (2.13) Sb (f ) = 0, f + fc < 0, where S(f ) is the power spectral density (PSD) of x(t). The relation between the two PSDs S(f ) and Sb (f ) are illustrated in Fig. 2.3. By substituting (2.12) into (2.8), we can obtain ⎫ ⎡⎧ ⎤ (t) ⎨N ⎬  √ y(t) = 2Re ⎣ ai (t)xb (t − τi (t))e−j2πfc τi (t) ej2πfc t ⎦ , (2.14) ⎩ ⎭ i=1

and express the baseband equivalent version of received signal as 

N (t)

yb (t) =

ai,b (t)xb (t − τi (t)),

(2.15)

i=1

where ai,b (t) = ai (t)e−j2πfc τi (t) is the complex attenuation of the i-th path.

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Discrete-Time Baseband Equivalent Model In designing signal processing techniques at the transceivers, it is often more convenient to consider the discrete-time signal model, which is typically obtained by sampling the continuous-time baseband signal. When xb (t) is bandlimited to W/2, the baseband continuous-time signal can be uniquely represented by the samples obtained at a sampling rate of W or higher. That is, given the discrete-time signal x[n] = xb (n/W ), for all n, the continuous-time signal can be reconstructed as  xb (t) = x[n]sinc(W t − n), (2.16) n

where sinc(t) = sin(πt)/(πt). Consequently, the equivalent baseband model for the output signal can also be written in terms of the samples {x[n]} as yb (t) =





N (t)

x[m]

m

ai,b (t)sinc(W t − W τi (t) − m).

(2.17)

i=1

The sample of yb (t) at time t = n/W can be expressed by   x[m] ai,b (n/W )sinc(n − m − W τi (n/W )) y[n]  yb (n/W ) = m

=



i

x[n − ]



ai,b (n/W )sinc( − W τi (n/W ))

i







x[n − ]h [n],

(2.18)



where {h [n], ∀ } is the impulse response of the discrete-time baseband equivalent channel observed at time t = n/W with the -th tap given by 

N (t)

h [n] =

ai,b (n/W )sinc( − W τi (n/W )).

(2.19)

i=1

Note that equation (2.18) can be also treated as a convolution between the time-varying channel sequence {h [n]} and the signal sequence {x[n]}.

Channel Coherence Time and Coherence Bandwidth Two important channel parameters have often been used to describe the temporal and spectral characteristics of the fading channel, namely, coherence time and coherence bandwidth. These parameters can be used to describe how fast the channel statistics vary over time and frequency, respectively.

2.1 Characteristics of Wireless Channels

23

Specifically, suppose that the time-domain and the frequency-domain representations of the baseband equivalent channel impulse response can be expressed as N (t)  hb (τ, t) = ai,b (t)δ(τ − τi (t)), (2.20) i=1

and 

N (t)

Hb (f, t) =

ai,b (t)e−j2πf τi (t)

(2.21)

i=1 N (t) 

=



 ai (t)e−j2πfc τi (t) e−j2πf τi (t) ,

(2.22)

i=1

respectively. The phase of each path (e.g., φi,b (t) = 2πfc τi (t) for the i-th path) can vary dramatically even for a small variation in the propagation delay τi (t) since the center frequency fc is typically large. The time-variation of τi (t) causes the phase φi,b (t) to change, resulting in a frequency-shift which we refer to as the Doppler effect. The Doppler shift of the i-th path is then defined as the amount of frequency-shift that occurs due to this time variation, i.e., 1 d Di  φi,b (t). 2π dt By considering the case where dτi (t)/dt = vi /c, where c is the velocity of light (i.e., the case where the propagation length of the i-th path varies with velocity vi over time), the Doppler shift is given by Di = fc vi /c. In multipath environments, the channel may contain multiple paths that experience different frequency shifts. Hence, we can define the so-called Doppler spread as the maximum difference between the Doppler shifts of different paths, i.e., Ds = max |Di − Dj |. i,j

(2.23)

Since the phase of the i-th path, i.e., φi,b (t), rotates by π/2 every 1/(4Di ) seconds, we can say that the impulse response hb (τ, t), which consists of multiple paths, may vary considerably over each interval of duration 1/(4Ds ) seconds. In other words, we can also say that the equivalent channel impulse response remains roughly the same over an interval that is much shorter than 1/(4Ds ) seconds. The coherence time of the channel is then defined as Tc = 1/(4Ds ). In practical systems, messages are often encoded into blocks of symbols and the encoding often depends on whether or not the channel varies over the symbol block. When the coherence time Tc is much larger than the transmission time of a symbol block, we say that the system is experiencing slow

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2 Review of Wireless Communications and MIMO Techniques

fading (or sometimes called block fading). In this case, the channel coefficient h [n] in (2.18) is often treated as a constant h throughout the symbol block. When Tc is much smaller than the transmission time of the symbol block, we say that the system is experiencing fast fading, in which case the channel coefficients may vary over each symbol period. By observing the channel frequency response in (2.22), one can see that, at any given time t, the phase difference between the i-th path and j-th path at frequencies, say f1 and f2 , can vary by π when |f1 − f2 | = 1/(2|τi (t) − τj (t)|). This shows that, as the multiple paths are more spread out in time, the faster the frequency response will vary over different frequencies. Let us define the delay spread as the maximum difference between the propagation delays of different paths, i.e., Td = max |τi (t) − τj (t)|. (2.24) i,j

In this case, we can say that the frequency response of the channel will have notable differences over frequencies separated by 1/(2Td). Therefore, the coherent bandwidth can be defined as Wc = 1/(2Td). Based on the channel variation characteristics in the frequency domain, we say that the channel is flat fading if the coherence bandwidth is much greater than the signal bandwidth, i.e., Wc  W , and say that it is frequency selective, otherwise. In the case of flat fading, the discrete-time channel (2.18) can be simplified by a single tap so that the received signal can be expressed as y[n] = h[n]x[n]. (2.25) In the case of frequency selective fading, the channel is represented by multiple taps as given in (2.18).

Statistical Models for Fading Coefficients Due to the large amount of uncertainties in the environment, it is convenient to model the channel statistically with a distribution given to each tap of the channel, i.e., the coefficients {h [n]} in (2.18). For simplicity, we assume in this section that the channel taps are independent and identically distributed (i.i.d.). More general models that consider correlation among channel taps can be found in [16]. Specifically, multipath fading in a rich scattering environment can be viewed as a collection of a large number of independent non-line-of-sight (NLOS) components. If the LOS path does not exist between the transmitter and receiver, each tap of the discrete channel impulse response in (2.19) will be a superposition of a large number of i.i.d. complex random variable. Thus, by central limit theorem, the real and imaginary parts of the channel coefficients can be approximated as i.i.d. zero-mean Gaussian

2.2 Techniques to Exploit Spatial Diversity

25

random variables. In this case, the envelope of the received signal will be Rayleigh distributed with PDF given by  2 2x x f (x) = exp − , x ≥ 0, (2.26) Ω Ω where Ω is the average received power determined by the path loss and shadowing phenomenon, and the power of the received signal will be exponentially distributed with mean Ω. If there exists a strong LOS path, the real and imaginary parts of the channel coefficients will again be i.i.d. Gaussian distributed but with mean that is no longer zero. Thus, the amplitude of the received signal is Rician distributed with PDF [24]      2x(K + 1) K(K + 1) (K + 1)x2 f (x) = exp −K − I0 2x , x ≥ 0, Ω Ω Ω (2.27) where Ω is the averaged received power in Rician fading, K is the Rician factor, and I0 (x) is the modified Bessel function of the first kind of zeroth order defined by  2π 1 I0 (x) = ex cos θ dθ. 2π 0 A more general channel model that better fits the experimental measurements of different environments is the Nakagami fading model [19]. In the so-called Nakagami-m fading channel, the envelop of the received signal is given by   2mm x2m−1 mx2 f (x) = , x ≥ 0, (2.28) exp − Γ(m)Ωm Ω where m ≥ 0.5 is the fading parameter, Γ is the Gamma function. When m = 1, Nakagami distribution is reduced to Rayleigh distribution and, when m = ∞, it is equivalent to the case without fading, i.e., a deterministic channel. In this book, we use Rayleigh fading as the representative scenario in many analysis on cooperative systems. Many of these analysis have also been conducted with Nakagami or other fading models in the literature. Readers will be referred to the appropriate literature when these topics arise (c.f. Chapter 4).

2.2 Techniques to Exploit Spatial Diversity In wireless systems, transmission failures occur mostly when the channel is in deep fade, resulting in the so called communication outage. To overcome this effect, one can exploit different kinds of diversity techniques in space,

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2 Review of Wireless Communications and MIMO Techniques

Fig. 2.4 Illustration of a SIMO system.

time, and frequency. In this section, we focus our discussions on the concept of spatial diversity since it is fundamental to the so-called cooperative diversity, which is to be discussed throughout this book. Spatial diversity can be exploited when the transmitter or the receiver have multiple antennas. Let us consider the flat-fading scenario, where no inter-symbol interference occurs over time, and assume that the antennas are located sufficiently apart from each other so that the channel coefficients between different transmitter and receiver antennas are statistically independent. Spatial diversity gains can be achieved with either precoding at the transmitter or signal combining at the destination. In the following, we will introduce these techniques for the three different scenarios, namely, single-input multiple-output (SIMO), multipleinput single-output (MISO), and multiple-input multiple-output (MIMO) systems. These techniques can also be extended to systems with diversity in other dimensions, such as the case of multipath or frequency selectivity.

2.2.1 Single-Input Multiple-Output (SIMO) System When the receiver is equipped with multiple antennas, we can take advantage of spatial diversity at the receiver to enhance system performance. Consider the system where a single-antenna transmitter sends data to a receiver with Nr antennas, as shown in Fig. 2.4. Let x[n]  be the symbol transmitted in the n-th symbol period and assume that E |x[n]|2 = 1. The signal received at the k-th antenna of the receiver in the n-th symbol period can be expressed as √ yk [n] = P hk x[n] + wk [n], (2.29) where P is the transmit power, hk is the channel coefficient observed by the k-th receive antenna, and wk [n] ∼ CN (0, σk2 ) is the additive white Gaussian

2.2 Techniques to Exploit Spatial Diversity

27

Fig. 2.5 Illustration of a linear combiner in a SIMO system.

noise (AWGN) at the k-th antenna. The channel coefficient hk can be written in terms of its amplitude |hk | and phase φk so that, for k = 1, 2, · · · , Nr , hk = |hk |ejφk . The SNR at the k-th antenna is then defined as γk 

P |hk |2 . σk2

(2.30)

Suppose that the instantaneous channel state information (CSI), i.e., the set of channel coefficients {h1 , h2 , · · · , hNr }, is known at the receiver. Before performing signal detection, the receiver will linearly combine the received symbols y1 [n], y2 [n], · · · , yNr [n] with the respective weighting factors α1 , α2 , · · · , αNr , as shown in Fig. 2.5, to obtain the signal z[n] =

Nr 

αk yk [n].

k=1

The values of weighting factors can be determined according to different signal combining techniques [3–5, 22]. Some common techniques are introduced in the following.

Equal-Gain Combining (EGC) One technique that is often considered is the equal-gain combiner (EGC), where the signals received on the Nr antennas are each multiplied by a complex weighting factor that compensates for the phase rotation of the channel. The complex weighting factors are given by

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2 Review of Wireless Communications and MIMO Techniques

αk = e−jφk ,

for k = 1, 2, · · · , Nr .

(2.31)

This technique achieves phase coherence at the receiver and, thus, increases considerably the received signal strength. Note that the magnitudes of the weighting factors |α1 |,|α2 |,· · · , |αNr | are the same and do not depend on the SNR values of all links. This reduces the complexity of the scheme compared to the maximal-ratio combining (MRC) scheme to be introduced later on. The output of the equal-gain combiner (EGC) is given by zEGC [n] =

Nr 

αk yk [n]

k=1

=

Nr  k=1

√ = P

√

e−jφk N r 



P |hk |ejφk x[n] + wk [n] 

|hk | x[n] +

k=1

Nr 

e−jφk wk [n].

(2.32)

k=1

Hence, the resulting SNR at the output of the equal-gain combiner can be given by  2     √ Nr |h | x[n] E  P  k k=1   γEGC =  2  Nr −jφk  E  k=1 e wk [n] P =



Nr k=1

Nr

2

|hk |

E[|x[n]|2 ]

E[|e−jφk wk [n]|]2 2 Nr P k=1 |hk | = . Nr 2 k=1 σk k=1



(2.33)

The outage probability of a SIMO system with EGC is demonstrated in Fig. 2.6 for the case of Rayleigh fading. The PDF of γEGC does not yield a closed-form in this case and, thus, the outage probability can only be evaluated numerically. In the computer simulation, the channel coefficients are assumed to be i.i.d. circularly symmetric Gaussian random variables with zero mean and unit variance. The noise variances at different receive anten2 nas are assumed to be identical so that σk2 = σw , ∀k. The SNR on the x-axis 2 is defined as SNR  P/σw . The threshold to determine outage events is set as γ0 = 1. We can see, in Fig. 2.6, that, due to co-phasing of the received signals, the decay rate of the outage probability will increase with the number of receive antennas, exploiting the spatial diversity at the receiver.

2.2 Techniques to Exploit Spatial Diversity

29

0

10

Nr=1 N =2 r

N =4

−1

r

10

Nr=8 N =10

Outage Probability

r

−2

10

−3

10

−4

10

0

5

10

15 SNR (dB)

20

25

30

Fig. 2.6 Outage probabilities of the equal-gain combining scheme.

Selection Combining (SC) Although the EGC scheme increases the SNR considerably at the receiver by co-phasing the signals, it is often difficult to achieve in practice since the phase of the channel varies rapidly over time and is difficult to track. When the signals cannot be perfectly co-phased, their addition may result in destructive interference and loss in spatial diversity. An alternative approach is to adopt the selection combining (SC) scheme where only the signal with the highest SNR among those received on the multiple antennas is utilized for detection. In this case, the weighting factors can be expressed as

1, if γk > γk , ∀k  = k, αk = (2.34) 0, otherwise, where γk  P |hk |2 /σk2 . Only the weight associated with the antenna with the highest SNR is equal to 1 while that of other antennas are equal to 0. The resulting SNR at the selection combiner (SC) output is equal to γSC =

max

k=1,··· ,Nr

γk .

(2.35)

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2 Review of Wireless Communications and MIMO Techniques

For example, let us consider the case with Rayleigh fading where the channel coefficients are assumed to be i.i.d. complex Gaussian with mean 0 and variance σh2 , i.e., hk ∼ CN (0, σh2 ), for all k, and the channel gains |hk |2 , for all k, are assumed to be exponentially distributed with mean 2 2 E[|hk |2 ] = σh2 . By letting σ12 = · · · = σN = σw , the received SNR at the r 2 2 k-th antenna, i.e., γk = P |hk | /σw , will also be exponentially distributed 2 with mean γ = P σh2 /σw . The PDF is given by fγk (u) =

1 − uγ e , γ

u ≥ 0, for k = 1, 2, · · · , Nr .

The outage probability, i.e., the probability that γSC falls below the required SNR at the receiver, say γ0 , can be computed as Pr (γSC

   Nr ≤ γ0 ) = Pr max γk ≤ γ0 = Pr (γk ≤ γ0 ) k

k=1

Nr    = 1 − e−γ0 /γ k=1

Nr  . = 1 − e−γ0 /γ

(2.36)

2  1, the outage When the transmit SNR is sufficiently high, i.e., γ = P σh2 /σw probability can be approximated as

 Pr (γSC ≤ γ0 ) ≈

γ0 γ

Nr

= γ0Nr e−Nr log γ .

By defining the diversity order as d  − lim

γ→∞

log Pr (γ ≤ γ0 ) , log γ

we can see that the SC scheme achieves diversity order of Nr , which is the number of receive antennas. In Fig. 2.7, we show the outage probability of the SIMO system with SC, where the simulation parameters are given as in Fig. 2.6. We can indeed observe that the diversity order of Nr is achieved at high SNR.

Maximal-Ratio Combining (MRC) Although EGC and SC utilize channel state information (CSI) to determine their weighting factors, the weighting factors applied in these schemes are not optimized in any sense. To fully exploit the spatial diversity provided by the multiple receive antennas, it is desirable to choose weighting factors that

2.2 Techniques to Exploit Spatial Diversity

31

0

10

Nr=1 N =2 r

N =4

−1

r

10

Nr=8 N =10

Outage Probability

r

−2

10

−3

10

−4

10

0

5

10

15 SNR (dB)

20

25

30

Fig. 2.7 Outage probabilities of the selection combining scheme.

maximize the receive SNR, which in order minimizes the outage probability. The scheme that achieves this task is referred to as the maximal-ratio combining (MRC) scheme. Specifically, given the instantaneous CSI, the weighting factors of the maximal-ratio combiner (MRC) can be given by αk = h∗k /σk2 = |hk |e−jφk /σk2 , for k = 1, . . . , Nr .

(2.37)

Here, the signals are weighted according to their local channel quality and are co-phased to achieve phase-coherent addition of signals at the receiver. The output of the MRC is given by zMRC [n] =

Nr  k=1

√ = P

αk

√  P hk x[n] + wk [n]

N r 

 αk hk

x[n] +

k=1

√ = P

 N r  |hk |2 k=1

The resulting SNR is computed as

σk2

Nr 

αk wk [n]

k=1

x[n] +

Nr  h∗

k

k=1

σk2

wk [n].

(2.38)

32

2 Review of Wireless Communications and MIMO Techniques

2

N r

γMRC =

N r

2

P |hk | |hk | P k=1   k=1 = 2 Nr  Nr   |hk |2 /σk2 E  (h∗k /σk2 )wk [n] k=1 2

/σk2

2

/σk2 =

Nr 

γk , (2.39)

k=1

k=1

which is essentially the sum of the received SNRs at all antennas. In fact, one can show that the MRC scheme achieves the maximum SNR among all linear combining techniques. Theorem 2.1. The output SNR of the linear combiner z[n] =

Nr 

αk yk [n]

k=1

is maximized with the set of coefficients αk = c · h∗k /σk2 , for k = 1, . . . , Nr ,

(2.40)

where c is an arbitrary constant. Proof. Given the weighting factors α1 , α2 , · · · , αNr , the output of the combiner can be expressed as  N Nr Nr r √  √    z= αk P hk x + wk = P αk hk x + αk wk . k=1

k=1

k=1

and the SNR is given by N r P | k=1 αk hk |2 P |αT h|2 , = γ = Nr 2 2 αH Σα k=1 |αk | σk

(2.41)

where α = [α1 , α2 , · · · , αNr ]T , h = [h1 , h2 , · · · , hNr ]T , and Σ = diag(σ12 , σ22 , 2 · · · , σN ). By Cauchy-Schwarz inequality, we can show that r γ= ≤

P |(Σ1/2 α∗ )H (Σ−1/2 h)|2 P |αT h|2 = αH Σα |Σ1/2 α|2 P |Σ1/2 α|2 |Σ−1/2 h|2 |Σ1/2 α|2

= P |Σ−1/2 h|2 =

Nr 

γk .

(2.42)

k=1

The equality holds when α = cΣ−1 h∗ , where c is an arbitrary constant. That is, the SNR is maximized when αk = c · h∗k /σk2 , for k = 1, . . . , Nr .  Suppose that the channels are i.i.d. Rayleigh distributed with mean σh2 2 2 and that the noise variances are identical, i.e., σ12 = · · · = σN = σw . In r

2.2 Techniques to Exploit Spatial Diversity

33

this case, the SNR of each link will be i.i.d. exponentially distributed with 2 mean γ = P σh2 /σw and, thus, the SNR at the output of the MRC, i.e., γMRC = γ1 + γ2 + · · · + γNr , can be modeled as a chi-squared random variable with 2Nr degrees of freedom. The mean and variance of γMRC is given by Nr γ and 2Nr γ, respectively. The PDF of γMRC can be written as fγMRC (u) =

uNr −1 e−u/γ , γ Nr (Nr − 1)!

u ≥ 0.

Consequently, the outage probability can be computed as 

γ0

Pr (γMRC ≤ γ0 ) =

fγMRC (u)du = 1 − e

−γ0 /γ

0

Nr  (γ0 /γ)k−1 k=1

(k − 1)!

.

(2.43)

By taking the Taylor expansion of the exponential term such that e−γ0 /γ =

∞  (−1)k (γ0 /γ)k k=0

k!

,

the term corresponding to the n-th power of γ0 /γ in (2.43), for 0 ≤ n ≤ Nr −1, can be computed as −

n  (−1)k (γ0 /γ)k k=0

k!

 (−1)k · 1(n−k) (γ0 /γ)n−k · =− (n − k)! k!(n − k)! k=0  n γ0 n = −(−1 + 1) γ n



γ0 γ

n

= 0. On the other hand, the term corresponding to the Nr -th power of γ0 /γ is computed as    Nr   Nr Nr Nr  1 (−1)k · 1(Nr −k) γ0 (−1)k · 1(Nr −k) γ0 + − = − k!(Nr − k)! γ k!(Nr − k)! Nr ! γ k=1 k=0   Nr 1 γ0 = . Nr ! γ Hence, the outage probability can be expressed as     Nr Nr +1 γ0 1 γ0 Pr (γMRC ≤ γ0 ) = . +O Nr ! γ γ

(2.44)

At high SNR, i.e., when γ  1, the first term in (2.44) dominates and, thus, the outage probability can be further approximated as

34

2 Review of Wireless Communications and MIMO Techniques

0

10

Nr=1 N =2 r

N =4

−1

r

10

Nr=8 N =10

Outage Probability

r

−2

10

−3

10

−4

10

0

5

10

15 SNR (dB)

20

25

30

Fig. 2.8 Outage probabilities of the maximal ratio combining scheme.

Pr (γMRC

γ Nr ≤ γ0 ) ≈ 0 e−Nr log γ = Nr !



γ0 2 (Nr !)1/Nr P σh2 /σw

Nr .

This shows that the MRC scheme achieves diversity order of Nr , similar to that of the SC scheme. However, a coding gain of (Nr !)1/Nr can be obtained, allowing the MRC scheme to achieve the same outage probability with (Nr !)1/Nr times less power than that required for the SC scheme. In Fig. 2.8, we show the outage probability of the MRC scheme with various number of receive antennas. The simulation parameters are the same as those considered in Figs. 2.6 and 2.7. By comparing the outage probabilities achieved with the MRC and the SC schemes, we can see that the MRC requires (Nr !)1/Nr times less power than the SC scheme in order to achieve the same outage probability. In other words, although both schemes achieve the same diversity order, the MRC scheme achieves an additional coding gain 10 equal to N log10 (Nr !) dB. The coding gain is obtained by the coherent comr bining as well as the optimal weighting of the received signals. The advantage increases with the number of receiver antennas, which can be verified by comparing the curves in Figs. 2.7 and 2.8. Furthermore, by comparing between Figs. 2.6 and 2.8, we can see that difference between the outage performance of the EGC and the MRC scheme is only within 1-2 dB since both schemes coherently combine the signals at the receiver.

2.2 Techniques to Exploit Spatial Diversity

35

Fig. 2.9 Illustration of a MISO system

2.2.2 Multiple-Input Single-Output (MISO) System When the transmitter is equipped with multiple antennas, the data symbols can be distributed among multiple transmit antennas to exploit spatial diversity at the transmitter. Here, we consider a system with Nt antennas at the transmitter and only a single antenna at the receiver, as shown in Fig. 2.9. Let {x[n]} be the sequence of data to be transmitted and assume that the data symbols are i.i.d. over time with zero mean and unit variance. Depending on the specific transmit diversity scheme, the data is first preprocessed to form a sequence of transmit symbol vectors {s[n]}, where s[n] = [s1 [n], s2 [n], . . . , sNt [n]]T is the vector of symbols to be transmitted over the Nt antennas in the n-th symbol period. The transmitted symbols are assumed to satisfy the sum power constraint E[|s|2 ] =

Nt 

E[|sk [n]|]2 ≤ 1.

(2.45)

k=1

The signal obtained at the receiver during the n-th symbol period is given by Nt  √ y[n] = P hk sk [n] + w[n], k=1

where P is the total transmit power, hk ∼ CN (0, σh2 ) is the channel coefficient between the k-th transmit antenna and the receiver, and w[n] is the AWGN 2 with zero mean and variance σw . Based on the different level of CSI at the transmitter, different signal processing techniques can be employed to exploit spatial diversity. These techniques are introduced in the following.

36

2 Review of Wireless Communications and MIMO Techniques

Fig. 2.10 Linear precoding (e.g., transmit beamforming) in a MISO system.

Transmit Beamforming (Full CSI) In the case of transmit beamforming, the data in each symbol period is multiplied by a set of weighting coefficients that precompensate for the channel effects before transmission. An illustration of transmit beamforming (or, the more general linear precoding scheme) is given in Fig. 2.10. Let β1 , β2 , · · · , βNt be the weighting factors imposed on the Nt antennas, respectively. The signal transmitted on the k-th antenna is given by sk [n] = βk x[n], where the transmission power is  2  √  Pk = E  P sk [n] = P · |βk |2 . With the linearly precoded transmit symbols, the signal observed at the receiver can be written as y[n] =

Nt  √

P hk βk x[n] + w[n].

(2.46)

k=1

The corresponding SNR at the receiver is then given by

γ=

2    Nt h k βk  P  k=1 2 σw

.

(2.47)

t When full instantaneous CSI (i.e., the channel coefficients {hk }N k=1 ) is Nt available at the transmitter, the set of weighting coefficients {βk }k=1 can be chosen to maximize the receive SNR given in (2.47) subject to the power constraint given in (2.45) [14]. The optimization problem can be formulated as follows:

2.2 Techniques to Exploit Spatial Diversity

37

2    Nt P  k=1 h k βk 

max

(2.48)

2 σw

β1 ,...,βNt

subject to

Nt 

|βk |2 ≤ 1.

(2.49)

k=1

Similar to the proof of Theorem 2.1, we can show, by utilizing the CauchySchwarz inequality, that N 2  N  N  Nt t t t        2 2 = h k βk  ≤ |hk | |βk | |hk |2 ,    k=1

k=1

k=1

k=1

where the equality holds when βk = c · h∗k , for k = 1, . . . , Nt . By choosing c to satisfy the power constraint in (2.49), we have h∗ βk =  k Nt

k =1

|hk |2

, for k = 1, . . . , Nt .

(2.50)

The corresponding SNR at the receiver is given by

γBFM =

2      Nt  hk h∗k P  N t 2  k=1 |h | k k =1 2 σw

=

Nt  P |hk |2 k=1

2 σw

.

(2.51)

Note that the SNR achieved with transmit beamforming is equal to the sum of the SNRs between each transmit antenna and the receiver, as if each transmit antenna takes turns in transmitting to the receiver at different instants in time and utilizing full power P in each transmission. This shows that transmit beamforming is able to achieve an Nt -fold performance gain compared to the SISO system. It is also worthwhile to note that the SNR achieved with transmit beamforming is the same as that obtained with MRC in SIMO systems and, thus, the outage performances will be identical as well. In fact, with full CSI at the transmitter, the MISO system can be viewed as a dual of the SIMO system and, thus, transmit beamforming is also referred to as the transmit MRC scheme. Although transmit beamforming is able to achieve a significant gain in SNR, the need of instantaneous CSI at the transmitter have hindered its use in practical systems. Several works in the literature have been done to address these issues. For example, one way to achieve this task is to have the receiver, which is able to estimate the CSI, compute the optimal beamforming coefficients and send it to the transmitter through a dedicated feedback channel. Due to rate limitations on the feedback channel, the set of optimal beamforming coefficients should be quantized before it is sent out through the

38

2 Review of Wireless Communications and MIMO Techniques

feedback channel [17, 18, 26, 38]. The codebook of the vector quantizer (VQ) should be designed to accurately represent the set of beamforming coefficients. These coefficients should be computed periodically with a frequency that depends on the time-variation of the channel. When the instantaneous CSI is not attainable, e.g., when the channel varies rapidly, the beamforming coefficients can instead be derived based on the statistics (rather than the instantaneous realizations) of the channel, as discussed in [25, 41].

Antenna Selection (Partial CSI) Suppose that the transmitter is only able to obtain knowledge of the channel amplitude but not the channel phase information. This case may arise in practice since the phase varies much faster than the channel amplitude and, thus, is more difficult to estimate. Without accurate knowledge of the phase information, the signals transmitted by different antennas cannot be co-phased at the receiver. In this case, it may be more desirable to transmit only on the antenna with the best channel in order to avoid destructive interference. This is referred to as the antenna selection scheme [28, 29]. The concept of the antenna selection is similar to that of the selection combining scheme in SIMO systems. Assume that the k ∗ -th antenna is the one experiencing the highest instantaneous SNR, i.e., P |hk |2 k ∗ = arg max , (2.52) 2 k σw and is selected to transmit so that βk = 1, for k = k ∗ , and βk = 0, for k = k ∗ . In this case, the signal obtained at the receiver is √ y[n] = P hk∗ x[n] + w[n], and the resulting SNR is given by γAS =

P |hk∗ |2 P |hk |2 = max . 2 2 k σw σw

(2.53)

The outage performance of the antenna selection scheme in MISO systems is the same as that of selection combining in SIMO systems since they achieve the same receive SNR. In practice, where channel can only be estimated at the receiver, antenna selection can be performed at the receiver and the index of the antenna with the best channel can be fed back to the transmitter.

2.2 Techniques to Exploit Spatial Diversity

39

Space-Time Coding (No CSI) When channel information is not available at the transmitter, spatial diversity cannot be fully exploited by encoding data only over the spatial dimension, as done in the previous linear precoding schemes, e.g., transmit beamforming and antenna selection. However, they can be exploited by encoding the data over both space and time, which results in the so called space-time coding (STC) scheme. The key idea can be demonstrated through the well-known Alamouti code example [1], as described below. Let us consider a MISO system with Nt = 2 antennas at the transmitter and a single antenna at the receiver. Let x[1] and x[2] be two consecutive information symbols and let E[|x[i]|]2 = 1, for i = 1, 2. By employing the Alamouti space-time coding scheme, the codewords transmitted by the two antennas over the two consecutive symbol periods can be written as     s [1] s2 [1] x[1] x[2] S 1 = , (2.54) −x∗ [2] x∗ [1] s1 [2] s2 [2] where sk [n], for k = 1, 2 and n = 1, 2, is the n-th symbol transmitted on the k-th antenna. Let P be the sum transmit power in each symbol period. We assume that P is equally allocated among transmit antennas since no CSI is available at the transmitter. The signals observed at the receiver in these two consecutive symbol periods are given by  P y[1] = (h1 s1 [1] + h2 s2 [1]) + w[1] 2  P (h1 x[1] + h2 x[2]) + w[1], = 2  P (h1 s1 [2] + h2 s2 [2]) + w[2] y[2] = 2  P (−h1 x∗ [2] + h2 x∗ [1]) + w[2], (2.55) = 2 where hk is the channel coefficient between the k-th transmit antenna and the 2 receiver, and w[n] ∼ CN (0, σw ) is the AWGN. Notice that the data symbols in y[2] are taken with respect to the complex conjugate of the original symbols. Therefore, by taking the complex conjugate of y[2], we can define the received signal vector as         P h 1 h2 y[1] w[1] x[1] y= ∗ + = y [2] w∗ [2] x[2] 2 h∗2 −h∗1  P Hx + w, (2.56) = 2 where

40

2 Review of Wireless Communications and MIMO Techniques



h 1 h2 H= h∗2 −h∗1

 (2.57)

is the effective channel matrix, x = [x[1], x[2]]T is the vector of data symbols, and w = [w[1], w∗ [2]]T is the AWGN vector with zero-mean and covariance 2 matrix Cw = σw I. By assuming that each symbol vector x in the signal 2 constellation X are transmitted with equal probability, the optimal decision on x can be given by the maximum likelihood (ML) detector, where the detected symbol vector is given by  ˆ = arg max f (y|x) = arg min y − x x∈X 2

x∈X 2

P Hx 2

2

.

Here, f (y|x) is the conditional density function of y given x. Notice that the ML detector reduces to the minimum distance detector in the AWGN channel. One disadvantage of the ML detector is that the decoding complexity increases exponentially with the length of the data vector. However, this issue is intelligently addressed with the Alamouti STC scheme, where the effective channel matrix in (2.57) can be shown to be orthogonal, i.e., HH H = (|h1 |2 + |h2 |2 )I2×2 .

(2.58)

In this case, we can multiply the received vector by the channel matrix to obtain z = HH y  P H H Hx + HH w = 2  P (|h1 |2 + |h2 |2 )x + w, = 2

(2.59)

where w = HH w is a zero-mean Gaussian noise vector with covariance matrix Cw = E[wwH ] = E[HH wwH H] 2 I2×2 )H = HH (σw 2 (|h1 |2 + |h2 |2 )I2×2 . = σw

(2.60)

Notice that the first and second entries of z (denoted by z[1] and z[2]) depend only on the symbols x[1] and x[2], respectively, and the entries of the noise HH w are uncorrelated. Therefore, the joint ML detection on x can be decoupled into two separate ML detectors performed on the scalar variables x[1] and x[2]. The optimal ML detector is given equivalently as  2    P   2 2 x ˆ[1] = arg min z[1] − (|h1 | + |h2 | )x[1]   2 x[1]∈X

2.2 Techniques to Exploit Spatial Diversity

and

41

 2    P   2 2 xˆ[1] = arg min z[2] − (|h1 | + |h2 | )x[2] .  2 x[1]∈X 

The key to achieving reduced decoding complexity is to construct spacetime codes that achieve orthogonality in the effective channel matrix H. The resultant SNR of each data symbol in (2.59) can be computed as P (|h1 |2 + |h2 |2 )2 2 (|h |2 + |h |2 ) 2σw 1 2 P (|h1 |2 + |h2 |2 ) = 2 2σw γ 1 + γ2 . = 2

γAla =

(2.61)

Compared with the transmit beamforming scheme, the SNR achieved with the Alamouti scheme is reduced by one halve. The decrease in SNR is expected since the transmitter does not have knowledge of the instantaneous CSI in the STC schemes. When considering Rayleigh fading channels, the SNR γAla can be modeled as a chi-square random variable with 4 degrees of freedom. Hence, it can be shown that the outage probability decays approximately as (1/¯ γ )2 , when E[γ1 ] = E[γ2 ] = γ¯ . This shows that diversity order of two is achieved with the Alamouti STC scheme, where instantaneous knowledge of the CSI is not required. Moreover, there is no loss in spectral efficiency compared to the SISO case since, on the average, one data symbol is transmitted in each symbol period. In the above example, we can see that spatial diversity is achieved with the STC scheme by encoding data symbols in both space and time so that each symbol can be transmitted over different antennas in different symbol periods. Two classes of space-time codes have been studied the most in the literature, namely, the class of space-time block codes (STBC) [1,13,15,32] and the class of space-time trellis codes (STTC) [33,39]. In STTC, the transmitted symbols are generated from a trellis diagram and thus the symbols contain memory. In STBC, the data symbols are encoded block-by-block into a sequence of T × Nt code matrices, where each code matrix is transmitted by Nt transmit antennas over T consecutive symbol periods. The decoding of both STTC and STBC is achieved with the ML detector (using the Viterbi algorithm in the case of STTC), which requires high decoding complexity. Let us consider in more detail the STBC scheme, where each block of M data symbols is encoded into a T × Nt codeword ⎤ ⎡ s1 [1] s2 [1] · · · sNt [1] ⎢ s1 [2] s2 [2] · · · sNt [2] ⎥ ⎥ ⎢ (2.62) S⎢ . .. ⎥ . .. . . ⎣ .. . . . ⎦ s1 [T ] s2 [T ] · · · sNt [T ]

42

2 Review of Wireless Communications and MIMO Techniques

Here, Nt is the number of transmit antennas and T is the length of codeword over time. Each codeword must satisfy the transmit power constraint E[ S 2F ] ≤ T , where S 2F = i,j |Si,j |2 represents the Frobenius norm of matrix S. In this case, the coding rate of the STC is equal to M/T symbols per channel use, which is equal to 1 in the case of Alamouti codes given in (2.54). Let y = [y[1], y[2], · · · , y[T ]]T be the vector of signals received over T symbol periods given by  P y= Sh + w, (2.63) Nt where h = [h1 , h2 , · · · , hNt ]T is the vector of channel coefficients on the Nt antennas and w is a T × 1 Gaussian white noise vector with correlation ma2 trix σw IT ×T . When each codeword is transmitted equally likely, the optimal decoding scheme is given by the ML decoding, i.e.,  ˆ = arg min y − S S∈S

P Sh Nt

2

,

(2.64)

where S is the set of all possible codewords. The ML decoding method can be easily extended to the case with multiple receive antennas. Although ML decoding is optimal, the decoding complexity in general increases exponentially with M . As demonstrated in the example on Alamouti codes, the decoding complexity can be reduced by considering codewords that allow the effective channel matrix to be orthogonal. This is achieved with code matrices whose columns are mutually orthogonal. This class of STBC is referred to as the orthogonal STBC (OSTBC) [1, 32]. One of the most well-known example among this class of codes is the Alamouti code mentioned previously. For a rate-one OSTBC design, where Nt = T = M (e.g. Alamouti code), the SNR at the receiver can be written as γOSTBC =

Nt P  |h |2 . 2 Nt σw =1

We can see that, with Nt transmit antennas in a MISO system, OSTBC achieves the same diversity order as transmit beamforming but with an SNR loss of 10 log10 Nt dB. Moreover, due to its orthogonal design, the optimal ML decoding can be decoupled into M scalar ML decoding operations through a simple linear transformation, which demands complexity that increases only linearly with M . Although OSTBC ideally is able to achieve full diversity as described above, it is not always possible to find a full-rate and full diversity code for arbitrary number of transmit antennas, especially when complex constellations are used. In fact, it has been shown that full-rate full diversity OSTBC exists only for Nt = 2, i.e., the Alamouti code [32]. To exploit spatial diver-

2.2 Techniques to Exploit Spatial Diversity

43

sity in more general cases, one may consider other STCs that can achieve full diversity with higher rate. In the following, we will introduce two general criteria for STC design. Suppose we consider the general STBC matrix given in (2.62) with the optimal ML detector given in (2.64). In this case, the pairwise error probability (PEP) between Si and Sj , where j = i, that is, the probability that the receiver detects Sj when Si was transmitted, is given by ⎛ ⎞   2 2 P P Sj h < y − Si h ⎠ Pr(Si → Sj |h) = Pr ⎝ y − Nt Nt ⎛ ⎞  2 P (Sj − Si )h < w 2 ⎠ = Pr ⎝ w − Nt  ' P hH Di,j h =Q 2 2Nt σw   P hH Di,j h , (2.65) ≤ exp − 2 4Nt σw where Di,j = (Sj − Si )H (Sj − Si ) is the non-negative definite distance matrix between codeword pairs Si and Sj . The inequality in (2.65) fol2 lows from the fact that Q(x) ≤ e−1/2x . Denote the eigenvalue decompoH sition of Di,j as Di,j = UΛU , where U is a Nt × Nt unitary matrix and Λ = diag(λ1 , λ2 , · · · , λNt ) is a diagonal matrix with non-negative entries. The PEP conditioned on the channel realization is then given by      Nt P hH UΛUH h P |b |2 λ = , (2.66) Pr(Si → Sj |h) ≤ exp − exp − 2 2 4Nt σw 4Nt σw =1

where b = [b1 , b2 , · · · , bNt ]T = UH h. Assume that the channel coefficients are i.i.d. circularly symmetric complex Gaussian distributed, i.e., h ∼ CN (0, σh2 INt ×Nt ). Since b comes from a unitary transform of h, b will have the same distribution as h and, thus, {|b |2 , ∀ } will be i.i.d. exponentially distributed with mean σh2 . Therefore, the PEP averaged over channel statistics is given by    P |b |2 λ Pr(Si → Sj ) ≤ E exp − 2 4Nt σw =1 −1 Nt   P λ σh2 1+ . = 2 4Nt σw Nt 

=1

At high SNR, the PEP can be approximately upper bounded by

(2.67)

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2 Review of Wireless Communications and MIMO Techniques

Pr(Si → Sj ) 

−1 r   P λ σ 2 =1

h 2 4Nt σw

 =

P σh2 2 4Nt σw

−r  r

λ−1  ,

(2.68)

=1

where r is the rank of the distance matrix Di,j . As SNR increases, it is observed from (2.68) that the overall error probability of the STC scheme decays exponentially with rate no less than rmin , which is the minimum rank of the distance matrices among all distinct codeword pairs. To optimize the error performance of the STC, two design criteria based on the PEP are given as follows [8]: 1. Rank criterion: The STC should be designed such that the minimum rank of the distance matrix between any pair of distinct codewords is as large as possible. An STC is able to achieve maximum diversity if the distance matrices among all pairs of distinct codewords are full rank. 2. Determinant criterion: The STC should be designed such that the minimum of the product of non-zero eigenvalues of all distance matrices is made as large as possible. When the distance matrices are full rank, this is equivalent to maximizing the minimum determinant of the distance matrices of all distinct codeword pairs. Since the minimum rank of distance matrices determines the diversity gain and the product of non-zero eigenvalues affects only the coding gain, one should place more emphasis on the rank criterion when designing space-time codes.

2.2.3 Multiple-Input Multiple-Output (MIMO) System When both the transmitter and the receiver are equipped with multiple antennas, both precoding at the transmitter and signal combining at the receiver can be employed to exploit the addition degrees of freedom. Suppose that there are Nt and Nr antennas at the transmitter and receiver, respectively, as shown in Fig. 2.11. Let s1 [n], s2 [n], · · · , sNt [n] be the symbols to be transmitted simultaneously over the Nt antennas in the n-th symbol period and assume that Nt   (2.69) E |sj [n]|2 ≤ 1. j=1

The signals observed at the Nr receive antennas are given, respectively, by √ y1 [n] = P (h1,1 s1 [n] + h2,1 s2 [n] + · · · + hNt ,1 sNt [n]) + w1 [n], √ y2 [n] = P (h1,2 s1 [n] + h2,2 s2 [n] + · · · + hNt ,2 sNt [n]) + w2 [n], .. .. . . √ yNr [n] = P (h1Nr s1 [n] + h2Nr s2 [n] + · · · + hNt Nr sNt [n]) + wNr [n],

2.2 Techniques to Exploit Spatial Diversity

45

Fig. 2.11 Multi-input-multi-output (MIMO) channel model.

where P is the total transmit power, hi,j is the channel coefficient between the 2 i-th transmit antenna and the j-th receive antenna and wj [n] ∼ CN (0, σw ) is the AWGN occurred at the j-th receive antenna. Let s[n] = [s1 [n], s2 [n], · · · , sNt [n]]T and y[n] = [y1 [n], y2 [n], · · · , yNr [n]]T be the input and output vectors of the MIMO channel. By writing the received signal in vector form, we have √ y[n] = P Hs[n] + w[n], (2.70) where H is an Nr × Nt channel matrix with each element being [H]ij = hj,i and w[n] = [w1 [n], w2 [n], · · · , wNr [n]]T is an additive Gaussian noise vector 2 with zero mean and covariance matrix σw INr ×Nr . With full CSI at both the transmitter and the receiver, we may exploit the knowledge of H to design the precoding scheme and the signal combining scheme at the transmitter and the receiver, respectively. Suppose that the rank of H is M , where M ≤ min(Nt , Nr ), it is possible to transmit M symbols at a time such that the receiver is able to successfully separate all transmitted symbols. Let us take the singular value decomposition (SVD) of the channel matrix to obtain H = UΣVH , (2.71) where U and V are Nr × Nr and Nt × Nt unitary matrices, and Σ is a Nr × Nt diagonal matrix composed of singular values of H in a decreasing order. Since the rank of H is M , there are M nonzero singular values, say μ1 ≥ μ2 ≥ · · · ≥ μM , on the diagonal of Σ. Let {x1 , x2 , · · · , xM } be a set of transmitted symbols. By assuming that H is known at the transmitter, the symbols can be precoded as s = Vx,

(2.72)

where x = [x1 , x2 , · · · , xM , 0, · · · , 0]T satisfies the transmit power constraint

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2 Review of Wireless Communications and MIMO Techniques

x1

s1

x2

s2

.. .

V

xM

sNt

y1

. . .

.. .

. . .

. . .

.. . H

z1

y2

z2

UH

.. .

zM

yNr

Fig. 2.12 Illustration of the precoding at the transmitter and linear combining at the receiver in MIMO channels.

¹1

x1 x2

.. . xM

z1

¹2

.. . ¹M

z2

.. . zM

Fig. 2.13 Illustration of equivalent parallel channels.

   E s 2 = E Vx 2 = E x 2 ≤ 1. Please note that we have dropped the symbol index n here simply for the ease of notation. At the receiver end, we exploit the SVD of H to separate the transmitted symbols. This is achieved by taking √  z = UH y = UH P Hs + w √ = P UH (UΣVH )Vx + w √ = P Σx + w, (2.73) where w = UH w is an equivalent noise vector with the covariance matrix 2 E[wwH ] = σw I. Since the matrix Σ and the covariance matrix of w are both diagonal, we have produced equivalently M parallel channels with uncorrelated noise as shown in Fig. 2.13. The parallel channels are also called the eigen-channels or eigen-modes of the MIMO channel. The output of the k-th eigen-channel is given by

2.2 Techniques to Exploit Spatial Diversity

zk =

√ P μk xk + w k ,

47

for k = 1, 2, · · · , M.

(2.74)

Thus, each non-zero diagonal element, say the k-th diagonal term μk , of Σ is the effective channel coefficient of the k-th parallel channel. Under the MIMO transmission scheme, if the number of nonzero elements in x is greater than rank(H) = M , some symbols are not received at the receiver since the symbols xM+1 , xM+2 , · · · , xMt are mapped to the null space of the channel matrix H by the linear transformations at the transmitter and the receiver. The transmitted symbols over the eigen-channels can be allocated with different power and encoded with different rates. The power allocated to the different eigen-channels can be further optimized according to the effective channel coefficients {μk } and based on different optimization criteria, such as the maximum capacity criterion [c.f. Section 2.3] or the minimum pairwise error probability criterion etc. Assume that the distance between any two transmit antennas or between any two receive antennas is sufficiently large such that all channel coefficients are independently distributed. More specifically, assume that {hi,j } are i.i.d. complex circularly symmetric Gaussian random variables with zero mean and variance σh2 , for i = 1, 2, · · · , Nt , and j = 1, 2, · · · , Nr . In the following, we will show an Nt × Nr MIMO channel is able to achieve diversity order of Nt Nr when CSI is available at both the transmitter and the receiver. Given the CSI, the transmitter is able to apply the aforementioned SVD technique to decompose the MIMO channel as M = rank(H) orthogonal eigen-channels. Consider an extreme case √ that a common symbol is transmitted over all eigenchannels, i.e., xk = x/ M , where E[|x|2 ] = 1. The signals received over all eigen-channels are combined with the MRC at the receiver, which results in the SNR at the output as γMIMO =

Nt  Nr M   P |μk |2 P |hi,j |2 = , 2 2 M σw M σw m=1 i=1 j=1 P σ2

which is chi-squared distributed with mean γ = σ2h and 2Nt Nr degrees of w freedom. The equality comes from the fact that summation of {|μi |2 }, which are eigenvalues of HHH , equals to the trace of HHH . Similar to (2.44), the corresponding outage probability can be simplified with Taylor expansion as in (2.44) and is expressed as   Nt Nr Nt Nr +1  1 M γ0 M γ0 Pr (γMIMO ≤ γ0 ) = (2.75) . +O (Nt Nr )! γ γ In high SNR regime, γ  1, the second term in (2.75) can be eliminated and the outage probability decays with SNR to the Nt Nr order. Hence, it shows that, when the channels between each transmit antenna and receive antenna are i.i.d., the diversity order of the MIMO channel is Nt Nr .

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2 Review of Wireless Communications and MIMO Techniques

However, it is sometimes prohibited for the transmitter to know the instantaneous CSI when the channel varies faster. If only the statistics of the channel coefficients are available at the transmitter, it is still possible to exploit the correlation matrix of the channel coefficients to design the pre-coder and decoder at the transmitter and receiver and the number of transmitted symbols allowed depends on the rank of the correlation matrix. When no CSI is available at the transmitter, we can still exploit spatial diversity by employing space-time codes at the transmitter, as shown in the MISO system, with the penalty of reduced coding gain at the receiver.

2.3 Capacity of Wireless Channels In this section, we study the fundamental limits of wireless channels in terms of its Shannon capacity [31], i.e., the maximum rate achievable between the transmitter and the receiver with arbitrary small error probabilities. The channel capacity is often used to characterize the limitations of a communication channel. In general, for a channel with input X and output Y , the capacity can be expressed as [6, (7.1)] C = max I(X; Y ),

(2.76)

p(x)

 ) ( p(x,y) is the mutual information between X where I(X; Y )  E log2 p(x)p(y) and Y . By this formula, one can view capacity as the maximum amount of information that the channel output can provide about the input message, when optimized over all possible input distributions p(x). In the following, we will briefly introduce the capacity of AWGN and wireless fading channels, and then extend to the case with multiple antenna transmitters and/or receivers.

2.3.1 Capacity of AWGN Channels Consider a single-input single-output AWGN channel with the input-output relations given by √ y = P x + w, where x is the complex channel input with unit power constraint E[|x|]2 ≤ 1, P is the transmit power employed by the transmitter, and w is the addi2 tive Gaussian noise with zero-mean and variance σw seen at the receiver. In AWGN channels, given the input x, the channel output y is Gaussian √ 2 distributed with mean P x and variance σw . By maximizing the mutual

2.3 Capacity of Wireless Channels

49

information over the input distribution, the capacity of AWGN channels is given by C = max I(X; Y ) p(x)   P = log2 1 + 2 σw = log2 (1 + SNR) bits per channel use,

(2.77) (2.78)

2 where SNR = P/σw is the transmit SNR at the transmitter, which equals to the SNR at the receiver in AWGN cases. Since the channel capacity is a function of SNR, the achievable transmission rate can be enhanced by increasing the value of the transmit SNR.

2.3.2 Capacity of Flat Fading Channels Consider the wireless fading scenario where the channel quality varies with time and space due to the effects of path-loss, shadowing and multipath fading effect, as introduced in previous sections. Here, we consider flat fading channels for simplicity. Since the channel coefficient is a random parameter, the capacity of a fading channel is random as well. For a given channel realization with channel coefficient h, the channel output is given by √ y = P hx + w, where x is the channel input with unit power constraint E[|x|]2 ≤ 1, P is the 2 transmit power, and w is the AWGN with zero-mean and variance σw . Under a given channel state h, the channel capacity can be expressed as   * + P |h|2 C = log2 1 + = log2 1 + SNR|h|2 . (2.79) 2 σw To further take into consideration the random variations of the channel, we can consider two capacity measures, one is the ergodic capacity, which is the rate achieved by encoding each message over multiple channel states, and the other is the outage capacity, which is the rate achieved under a constraint on the outage probability. These measures are introduced in the following.

Ergodic Capacity Ergodic capacity is defined as the capacity achieved by encoding across multiple channel realizations. In the case of Gaussian inputs, the ergodic capacity can be characterized as

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2 Review of Wireless Communications and MIMO Techniques

 C = E|h|2 log2 (1 + SNR|h|2 )  ∞ = f|h|2 (u) log2 (1 + SNRu)du.

(2.80)

0

When CSI is only available at the receiver, ergodic capacity can be achieved by coding across sufficiently large number of channel realizations. When the CSI is also available at the transmitter and the transmission power is fixed, the ergodic capacity will be the same as that in the case with only receiver-side CSI. In general, with transmitter-side CSI, power can be optimally allocated over time, using the water-filling algorithm, to further increase the ergodic capacity. However, no significant gains can be obtained by doing so [12].

Outage Capacity Outage capacity is another popular performance measure for communication techniques in fading channels. Outage is the event that the message cannot be reliably decoded at the receiver. From an information theoretic standpoint, given the transmission rate R and the channel realization h, we say that outage * occurs if +the channel capacity is less than the transmission rate, i.e., log2 1 + SNR|h|2 < R. Hence, outage probability under the channel realization h can be expressed as a function of the transmission rate as given below: * + pout (R)  Pr log2 (1 + SNR|h|2 ) < R * + = Pr SNR|h|2 < 2R − 1 . (2.81) The -outage capacity is then defined as the maximum transmission rate that can be achieved over the fading channel such that the outage probability is smaller than , i.e., C =

arg max {R:R≥0,pout (R)≤ }

pout (R)

(2.82)

where  ∈ [0, 1] is a constant threshold. The outage probability can be calculated by using the PDF of the channel coefficient or the received SNR. For example, in the Rayleigh channel where the envelope of channel coefficient is Rayleigh distributed and the receive SNR SNR|h|2 is exponentially distributed with mean SNRσh2 , the outage probability of the Rayleigh channel given the transmission rate R is given by * + pout (R) = Pr SNR|h|2 < 2R − 1   R 2 −1 . = 1 − exp − SNRσh2

2.3 Capacity of Wireless Channels

51

3.5

3

AWGN capacity Rayleigh, Ergodic Capacity Rayleigh, 10% Outage Capacity

C (bits/sec/Hz)

2.5

2

1.5

1

0.5

0 0

1

2

3

4

5 SNR (dB)

6

7

8

9

10

Fig. 2.14 Ergodic capacity and outage capacity in a fading channel and capacity of an AWGN channel.

In general, outage capacity can be obtained from the inverse of the outage probability function, i.e., p−1 out (·), or can be numerically found from the outage probability curve. The capacity of an AWGN channel and the ergodic and outage capacities of a Rayleigh fading channel are compared in Fig. 2.14. The variance of the fading channel coefficient is set as one. We can see that, in the case of Rayleigh fading, the 10%-outage capacity is much smaller than the ergodic capacity. Moreover, we can also see that, even though the average SNR is the same for both the fading and the AWGN channels, the ergodic capacity of the fading channel is still inferior to that of the AWGN channel capacity. This is because, even though the SNR can be occasionally high in the fading case, the gain in capacity in these cases are not proportional due to the concavity of the logarithmic function. This follows mathematically by the Jensen’s inequality when comparing the two capacity expressions. From the definition of outage probability when the channel input is with the rate C , reliable transmission can be done with outage probability . The outage capacity is more practical to measure the channel capacity, since it provides a certain degree of guarantee on the reliable communication. In the following, we consider both outage capacity and ergodic capacity of channels with multiple transmit and/or receive antennas.

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2 Review of Wireless Communications and MIMO Techniques

12

10

C (bits/sec/Hz)

8

6

4 Nt=1, Nr=1 N =1, N =2

2

t

r

Nt=1, Nr=3 Nt=1, Nr=4 0 0

5

10

15 SNR (dB)

20

25

30

Fig. 2.15 Ergodic capacity of a single-input multiple-output (SIMO) channel.

2.3.3 Capacity with Multiple Antennas Consider a SIMO system with a single antenna at the transmitter and Nr antennas at the receiver. Here, we assume that the AWGN at each receiver 2 is i.i.d. Gaussian with zero-mean and variance σw . The received signals at all antennas can be combined using the MRC to achieve the maximum receive SNR. The resulting SNR is given by γ=

Nr  P |hk |2 k=1

2 σw

= SNR

Nr 

|hk |2 ,

j=1

2 is the SNR at the transmitter, hk is the channel coeffiwhere SNR = P/σw cient between the transmit antenna and the k-th receive antenna. In a fading r channel, the channel coefficients {hk }N k=1 are random. Given the set of channel coefficients {h1 , h2 , · · · , hNr }, the channel capacity of the SIMO channel is given by   Nr  2 C = log2 1 + SNR (2.83) |hk | . k=1

The receive SNRs obtained in Rayleigh and Rician fading channels take on central and non-central chi-squared distributions, respectively. The ergodic capacity or outage capacity can then be found from the distribution of the received SNR. The ergodic capacity and the 10%-outage capacity of a

2.3 Capacity of Wireless Channels

53

12 N =1, N =1 t

r

N =1, N =2 t

10

r

Nt=1, Nr=3 Nt=1, Nr=4

C (bits/sec/Hz)

8

6

4

2

0 0

5

10

15 SNR (dB)

20

25

30

Fig. 2.16 Outage capacity of a single-input multiple-output (SIMO) channel.

SIMO channel under Rayleigh fading are illustrated in Figs. 2.15 and 2.16, respectively. The variances of all channel coefficients are set to 1. The figures show that channel capacity increases linearly with the transmit SNR (in dB scale) and the increasing rate of channel capacity is the same regardless of the number of receive antennas. In a MISO system with Nt transmit antennas and one receive antenna, the transmitted signals are linearly combined at the receiver and the maximal received SNR depends on the availability of CSI at the transmitter. When full CSI is available at the transmitter, the data symbol can be multiplied by a set of beamforming factors to maximize the received SNR. The resulting SNR at the receiver is the same with the received SNR of a SIMO system. Thus, the ergodic capacity and outage capacity of a MISO system with full CSI at the transmitter have the same performance as the SIMO system in Figs. 2.15 and 2.16. On the other hand, when the channel information is not available at the transmitter, we may apply space-time codes to exploit spatial diversity. The resulting SNR is only (1/Nt ) times that in the case with full CSI at the transmitter. Therefore, given the channel coefficients {hk }, the channel capacity of a MISO system is given by   Nt SNR  (2.84) C = log2 1 + |hk |2 , Nt k=1

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2 Review of Wireless Communications and MIMO Techniques

10 9 8

C (bits/sec/Hz)

7 6 5 4 3 Nt=1, Nr=1 2

N =2, N =1

1

Nt=3, Nr=1

t

r

Nt=4, Nr=1 0 0

5

10

15 SNR (dB)

20

25

30

Fig. 2.17 Ergodic capacity of a multiple-input single-output (MISO) channel.

where hk is the channel coefficient between the k-th transmit antenna and the receive antenna. Under the Rayleigh or Rician fading assumption, the receive SNR of a MISO system is also chi-squared distributed and we can use a similar method to find the ergodic capacity and outage capacity. For the Rayleigh fading environment, the ergodic capacity and the outage capacity of a MISO system without CSI at the transmitter are shown in Figs. 2.17 and 2.18, respectively. In the simulations, the variances of all channel coefficients are set to 1. Similar to the results in SIMO systems, the capacity of different numbers of transmit antennas increase with the transmit SNR at the same rate in the MISO system. Compared with the results of the SIMO system shown in Figs. 2.15 and 2.16, we can see that the capacity of MISO systems without CSI at the transmitter is degraded due to the decreased SNR at the receiver. Moreover, it is worthwhile to note that the ergodic capacity of MISO systems without CSI at the transmitter is upper bounded as      N Nt t  SNR  SNR 2 2 E log 1 + ≤ log 1 + |hk | E |hk | Nt Nt k=1

k=1

= log (1 + SNR) = CAWGN . In a MIMO system where the transmitter and the receiver have Nt and Nr antennas, respectively, the channel capacity depends on the availability of CSI at the transmitter, too. When the transmitter has channel information, the input symbols can be further designed to achieve the optimal transmission

2.3 Capacity of Wireless Channels

55

9 N =1, N =1 t

8 7

r

N =2, N =1 t

r

Nt=3, Nr=1 Nt=4, Nr=1

C (bits/sec/Hz)

6 5 4 3 2 1 0 0

5

10

15 SNR (dB)

20

25

30

Fig. 2.18 Outage capacity of a multiple-input single-output (MISO) channel.

rate. Let us consider a static channel state H, where the (j, i)-th element {H}j,i = hi,j is the channel coefficient between the i-th transmit antenna and the j-th receive antenna. With full CSI at transmitter, the channel capacity is given by [10, 34]    P C= max , (2.85) log2 det INr ×Nr + 2 HRs HH Rs :Tr(Rs )=1 σw where Rs = E[ssH ] is the covariance of the input signals, Tr(Rs ) = 1 indicates the power constraint, and the optimization is performed over the semi-positive definite covariance matrix of the input symbols. Recall that, in Section 2.2, we take the SVD of the channel matrix to decompose the MIMO system into a set of parallel channels. Let the SVD of the channel matrix be H = UΣVH , where U and V are Nr × Nr and Nt × Nt unitary matrices, and Σ is a diagonal matrix composed of singular values in a decreasing order. As shown in Figs. 2.12 and 2.13, by transmitting the pre-coded signals s = Vx at the transmitter and decoding with z = UH x at the receiver, the MIMO channel can be decomposed into M = rank(H) parallel channels. The k-th decomposed channel can be represented by √ zk = P μk xk + w k , for k = 1, 2, · · · , M, where μk is the effective channel coefficient of the k-th decomposed channel and is also k-th element of the diagonal matrix Σ. With the pre-coding and

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2 Review of Wireless Communications and MIMO Techniques

decoding schemes, the mutual information between the pre-coder input x and decoder output z equals to the sum of capacity of M parallel channels, i.e., I(x; z) =

M  k=1

  Pk |μk |2 , log2 1 + 2 σw

(2.86)

√ where Pk = E[| P xk |2 ] is the equivalent transmission power of the k-th parallel channel. The scheme is equivalent to the case where the input covariance is set as P · Rs = VPVH , where P = diag(P1 , P2 , . . . , PM , 0, . . . , 0). To satisfy the power constraint, we have Tr(P · Rs ) = Tr(P) = P1 + P2 + · · · + PM = P.

(2.87)

The powers allocated on the M inputs of the parallel channels can be further optimized to achieve the maximum channel capacity. That is, the optimization problem can be formulated as max

M 

P1 ,··· ,PM

k=1

  Pk |μk |2 , log2 1 + 2 σw

subject to P1 + P2 + · · · + PM ≤ P ; Pk ≥ 0, ∀k. To find the optimal solution, we use the Lagrangian function given as M    M   Pk |μk |2 −λ log2 1 + Pk − P , (2.88) L(P1 , · · · , PM , λ) = 2 σw k=1

k=1

where λ is a Lagrange multiplier set to meet the sum power constraint. The optimum set of transmission powers can be found by taking the first derivative of the Lagrangian function with respect to P1 , P2 , · · · , PM . Moreover, since the transmission powers {Pk , ∀k} are non-negative, the optimal power allocation must satisfy the Karush-Kuhn-Tucker (KKT) conditions where, for k = 1, · · · , K, it follows that ∂L(P1 , · · · , PM , λ) = 0, ∂Pk if Pk > 0, and

∂L(P1 , · · · , PM , λ) ≤ 0, ∂Pk

if Pk = 0. To be more specific, if the first derivative of the Lagrange function with respect to Pk is negative at the point where Pk = 0, the maximum value of the Lagrange function occurs at a negative value of Pk and the Lagrangian function will monotonically decreasing with respect to Pk for Pk ≥ 0. The optimal value of Pk in this case is 0. Otherwise, the optimal Pk is positive and can be found by setting the first derivative to zero. The first derivative

2.3 Capacity of Wireless Channels

57

Fig. 2.19 Illustration of waterfilling solution in (2.90).

of L(P1 , · · · , PM , λ) with respective to Pk is given by −1  Pk |μk |2 |μk |2 ∂L(P1 , · · · , PM , λ) = (log2 e) · 1 + · 2 − λ. 2 ∂Pk σw σw Following the KKT conditions, the optimal transmission power is , 2 ≤ λ, 0, if (log2 e)|μk |2 /σw 2 Pk = log2 e , σw − , otherwise. λ |μk |2 +  σ2 , k = 1, 2, · · · , M, = η − w2 |μk |

(2.89)

(2.90)

where η = log2 e/λ is a constant set to meet the sum power constraint in (2.87) and (x)+ = max(x, 0). The value of η can be found by the water-filling algorithm, as illustrated in Fig. 2.19, and the optimal solution in (2.90) is referred to as the water-filling solution. With the optimal pre-coder and the optimal decoder, the channel capacity is given by    η|μk |2 C= . (2.91) log2 2 σw k:Pk >0

Consider the fading channel for example again, if the transmitter has knowledge of the instantaneous channel state H, the covariance Rs of channel input symbols can always be adapted accordingly to achieve the channel capacity. Therefore, the ergodic capacity of a MIMO system with full CSI at the transmitter is given by

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2 Review of Wireless Communications and MIMO Techniques

 C = EH

max

Rs :Tr(Rs )=1

*



log2 det INr ×Nr + SNRHRs H

H

 +

.

(2.92)

If the transmitter knows only the channel distribution, the covariance matrix of the channel input symbols can be set to maximize the average channel capacity by exploiting the channel statistics. Therefore the ergodic capacity of the MIMO system with statistical CSI at the transmitter is given by  *  + C= max EH log2 det INr ×Nr + SNRHRs HH . (2.93) Rs :Tr(Rs )=1

On the contrary, if the transmitter does not have any channel information, it can simply set the input symbols’ covariance by Rs = N1t INt ×Nt and the resulting ergodic capacity is given by     SNR H C = EH log2 det INr ×Nr + . (2.94) HH Nt It can be verified that, in high SNR regime, the ergodic capacity can be approximated by [10] C ∼ min(Nt , Nr ) log2 SNR + O(1),

(2.95)

which implies that in a MIMO system where no CSI is available at the transmitter, the ergodic capacity increases linearly with the transmit SNR (in dB) with rate min(Nt , Nr ). It is worthy to note that the increasing rate of the ergodic capacity in MISO systems does not depend on the number of transmit antennas since min(Nt , Nr ) = 1. For MIMO systems where no CSI is available at the transmitter, the ergodic capacity of various numbers of transmit antennas and receive antennas are compared in Fig. 2.20. Each channel coefficient is i.i.d. Gaussian with CN (0, 1). It can be observed that the increase in the ergodic capacity is proportional to the minimum number between the numbers of transmit antennas and receive antennas. Besides, without CSI at the transmitter, increasing the number of receive antennas is more efficient than increasing that of transmit antennas.

2.4 Diversity-and-Multiplexing Tradeoff A fundamental concept in MIMO wireless communications is the inherent tradeoff between diversity and multiplexing gains. It has been shown that, to increase diversity gains, one must tradeoff certain degrees of freedom available in the system and sacrifice the ability to achieve higher transmission rates. Similarly, in order to achieve a faster increase in transmission rate, with respect to the transmit power, one must tradeoff the exponential decay rate of the error probability. These concepts are introduced in this section.

2.4 Diversity-and-Multiplexing Tradeoff

59

35 N =1, N =1 t

30

25

r

N =1, N =2 t

r

Nt=2, Nr=1 Nt=2, Nr=2 N =4, N =4

C (bits/sec/Hz)

t

r

20

15

10

5

0 0

5

10

15 SNR (dB)

20

25

30

Fig. 2.20 Ergodic capacity of a multiple-input multiple-output (MIMO) channel where the CSI is only available at the receiver.

Let us start with a simple SISO system with a Rayleigh-faded channel. The signal received at the receiver is given by √ y[m] = P hx[m] + w[m], (2.96) where P is the transmission power, h ∼ CN (0, 1) is the channel coefficient which is a complex Gaussian random variable, w[m] is the AWGN with zero 2 mean and variance σw , and x[m] is the transmitted symbol with unit power constraint, i.e., E[|x[m]|2 ] ≤ 1. Suppose that the transmitted symbols are modulated with pulse amplitude modulation (PAM). For instance, the PAM constellation points with data rate R = 1 bits/sec/Hz are {±1} (i.e., BPSK modulation), and the average symbol error probability is given by    ( ) 1 SNR 2 pe = E|h|2 Q 1− 2|h| SNR = 2 1 + SNR '   1 (d/2)2 1− , (2.97) = 2 1 + (d/2)2 √ 2 where SNR = P/σw is the transmit SNR and d = 2 SNR is the Euclidean distance between the two signal points. In general, for√a PAM scheme with √ rate R, there are 2R points ranging from − SNR to + SNR on the constellation, and the minimum distance between any two constellation points can

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2 Review of Wireless Communications and MIMO Techniques

be approximated by

√ 2 SNR , 2R when R is large. The error probability for the SISO system with rate-R PAM can be approximated in terms of the minimum distance by ' '     2 (Dmin /2)2 Dmin 1 1 1− = 1− . (2.98) pe ≈ 2 2 (Dmin /2)2 + 1 2 Dmin +4 Dmin ≈

At high SNR, the minimum distance Dmin is sufficiently large and, by Taylor approximation, we can further approximate the error probability in (2.98) as '   2 1 1 22R−2 Dmin pe ≈ 1− ≈ 2 = . (2.99) 2 2 Dmin + 4 Dmin SNR From (2.99), we can find that the error probability is a function of the data rate R and the SNR. If the transmit SNR is increased by 4 times (i.e., P  = 4P ) and the data rate R is fixed, the minimum distance is enlarged and the error probability are quartered, i.e., pe = pe /4. On the other hand, we may fixed the error probability, i.e., the minimum distance, and we can double the number of points in the constellation to increase the transmission rate by one, i.e., R = R + 1. Therefore, the increase in the transmit power can either decreases the error probability by one quarter or increases the transmission rate by one. Hence, we can have the transmission rate increased by ΔR < 1 and the error probability decreased to pe > pe /4 by using time-sharing among the two extreme cases. As the increase in data rate approaches one, we trade the error performance for extra data rates. From this example, it is observed that there is a tradeoff between the decrease in error probability and the increase in data rate as the transmit SNR increases. The tradeoff is termed the diversity-and-multiplexing tradeoff (DMT). Before taking a deeper look at the DMT, let us define the diversity gain and multiplexing gain mathematically. Definition 2.1. The diversity gain of a system with the average error probability in terms of the transmit SNR, i.e., pe (SNR), is defined by d = − lim

SNR→∞

log pe (SNR) . log SNR

(2.100)

The diversity gain of a system characterizes how the average error probability decreases with the SNR. To be more specific, a communication system achieves diversity gain d if, in the high SNR regime, the average error probability scales as pe (SNR) ∼ c · SNR−d , for some constant c that is independent of the SNR. That is, in the high SNR regime, increasing the transmit SNR by 3 dB leads to the decrease of

2.4 Diversity-and-Multiplexing Tradeoff

61

error probability by c · 2−d . In the literature, the error probability in (2.100) is sometimes replaced with the outage probability. Both probabilities have been used to define diversity gains. For example, consider a SISO channel with PAM modulation, the maximum diversity is dmax = 1 since the error probability decays with the inverse of SNR as given in (2.99). For a MIMO system with Nt transmit antennas and Nr receive antennas, the maximum diversity gain in general is shown to be d∗max = Nt × Nr , as given in (2.75) The notion of multiplexing gain is defined as follows. Definition 2.2. The multiplexing gain of a system with the achievable rate in terms of transmit SNR, i.e., R(SNR), is defined by r = lim

SNR→∞

R(SNR) . log SNR

(2.101)

To be specific, in the high SNR regime, the transmission rate of a communication system with multiplexing gain r scales as R(SNR) ∼ r · log(SNR). In other words, for a system with multiplexing gain r, each 3 dB increase in the transmit SNR leads to the increase of transmission rate by r bits/sec/Hz. The multiplexing gain characterizes the proportional increase of transmission rate with respect to log SNR when the asymptotic decay rate of the error probability (or the outage probability) does not change. The maximum multiplexing gain is achieved when the error probability is fixed at any SNR. For example, the maximum multiplexing gain for a SISO system with PAM modulation equals to rmax = 1/2. For a MIMO channel with Nt transmit antennas, Nr receive antennas, and no CSI at the transmitter, the ergodic capacity at high SNR under Rayleigh fading has been given in (2.95). The ergodic capacity can be viewed as the achievable rate for an arbitrary small probability of errors. From the approximation in (2.95), the maximum multiplexing gain of a MIMO channel equals to ∗ rmax = min(Nt , Nr ).

When Nt = Nr = 1, the maximum multiplexing gain is one, which means that the uncoded transmission scheme modulated with PAM does not achieve the highest multiplexing gain of the SISO channel. By the PAM example, we know that we can not achieve maximum diversity gain and maximum multiplexing gain at the same time. For a channel with ∗ a given multiplexing gain r ≤ rmax , the transmission rate at high SNR is R(SNR) = r · log SNR and the diversity gain can be obtained from the error performance or outage performance of the system. The relationship between the diversity gain and the multiplexing gain is mathematically defined as follows.

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2 Review of Wireless Communications and MIMO Techniques

Definition 2.3. Given a multiplexing rate r, a diversity gain d(r) is achieved if, in high SNR regime, R(SNR) = r · log(SNR) and

log pe (SNR) . log SNR

d(r) = − lim

SNR→∞

(2.102)

(2.103)

Consider a SISO system with PAM modulation, for example, the maximum diversity gain is dmax = 1 and the maximum multiplexing gain is rmax = 1/2. In this case, a multiplexing gain of r ≤ 1/2 can be achieved by time-sharing among the two cases. The resulting diversity gain is given by d(r) = 1 − 2r,

0 ≤ r ≤ 1/2.

For a general SISO system with Rayleigh fading coefficient h ∼ CN (0, 1), the error probability for a given rate R = r log SNR is . / pe = Pr log(1 + |h|2 SNR) < r log SNR 0

SNRr − 1 2 = Pr |h| < SNR   SNRr − 1 = 1 − exp − SNR 1 ≈ . SNR1−r Hence, the optimal diversity-and-multiplexing tradeoff of a general SISO channel, under the Rayleigh fading environment, is given by d∗ (r) = 1 − r,

r ∈ [0, 1].

(2.104)

For a MISO system with Nt transmit antennas, the error probability decays Nt times faster than that in a general SISO system. The diversity-andmultiplexing tradeoff for the MISO system is given by d∗ (r) = Nt (1 − r),

r ∈ [0, 1].

One can draw the points (r, d∗ (r)), for r ∈ (0, 1), on a graph to gain knowledge of the relationship between diversity and multiplexing gains. For SISO and MISO system, the optimum DMT is just a straight line. The endpoints of the line are the point with maximum diversity gain and the point with maximum multiplexing gain. On the other hand, for a MIMO system with Nt transmit antennas and Nr receive antennas, the achievable diversity-and-multiplexing tradeoff is proven to be [40]

References

63

d∗ (r) = (Nt − r)(Nr − r),

r = 0, 1, · · · , min(Nt , Nr )

(2.105)

By time-sharing of the cases with integer multiplexing gains, the curve of the maximum DMT is piecewise-linear connecting the points (r, (Nt − r)(Nr − r)),

r = 0, 1, · · · , min(Nt , Nr ).

With r → 0, the maximum diversity gain Nt Nr is achieved with very low multiplexing gain. As r → min(Nt , Nr ), we have the maximum multiplexing gain but the diversity gain is 0. Moreover, for a given diversity order, the multiplexing gain is increased by one only when both numbers of the transmit and receive antennas are increase by one simultaneously.

References 1. Alamouti, S.M.: A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications 16(8), 1451–1458 (1998) 2. Bernhardt, R.: Macroscopic diversity in frequency reuse radio systems. IEEE Journal on Selected Areas in Communications 5(5), 862–870 (1999) 3. Beverage, H.H., Peterson, H.O.: Diversity receiving system of RCA communications, inc., for radiotelegraphy. In: Proceedings of IRE, pp. 531–561 (1931) 4. Brennan, D.: Linear diversity combining techniques. Proceddings of the IEEE 91(2), 331–356 (2003) 5. Brennan, D.G.: On the maximal signal-to-noise ratio realizable from several noisy signals. In: Proceedings of IRE, p. 1530 (1955) 6. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2 edn. Wiley-Interscience (2006) 7. Cox, D., Murray, R., Norris, A.: 800 MHz attenuation measured in and around suburban houses. AT&T Bell Laboratory Technical Journal 63(6), 921–954 (1984) 8. Duman, T.M., Ghrayeb, A.: Coding for MIMO Communication Systems, John-Wiley & Sons Ltd. (2007) 9. Erceg, V., Greenstein, L., Tjandra, S., Parkoff, S., Gupta, A., Kulic, B., Julius, A.A., Bianchi, R.: An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on Selected Areas in Communications 17(7), 1205–1211 (1999) 10. Foschini, G.J.: Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell System Technical Journal 1, 44–59 (1996) 11. Goldsmith, A.: Wireless Communications. Cambridge University Press (2005) 12. Goldsmith, A.J., Varaiya, P.P.: Capacity of fading channels with channel side information. IEEE Transactions on Information Theory 43(6), 1986–1992 (1997) 13. Hassibi, B., Hochwald, B.: High-rate codes that are linear in space and time. IEEE Transactions on Information Theory 48(7), 1804–1824 (2002) 14. Jafar, S.A., Goldsmith, A.: On optimality of beamforming for multiple antenna systems. In: Proc. on IEEE International Symposium on Information Theory (ISIT), pp. 321 (2001) 15. Jafarkhani, H.: A quasi-orthogonal space-time block code. IEEE Transactions on Communications 49(1), 1–4 (2001) 16. Jakes, W.C.: Microwave Mobile Communications, 2 edn. IEEE Press (1994)

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17. Love, D.J., Heath, Jr., R.W., Strohmer, T.: Grassmannian beamforming for multipleinput multiple-output wireless systems. IEEE Transactions on Information Theory 49(10), 2735–2747 (2003) 18. Mukkavilli, K., Sabharwal, A., Erkip, E., Aazhang, B.: On beamforming with finite rate feedback in multiple-antenna systems. IEEE Transactions on Information Theory 49(10), 2562–2579 (2003) 19. Nakagami, N.: The m-distribution, a general formula for intensity distribution of rapid fading. Statistical Methods in Radio Wave Propagation, Edited by W. G. Hoffman, Oxford, England (1960) 20. Owen, F., Pudney, C.: Radio propagation for digital cordless telephones at 1700 MHz and 900 MHz. IEEE Electronics Letters 25(1), 52–53 (1989) 21. Parsons, D.: The Mobile Radio Propagation Channel. Willey (1994) 22. Peterson, H.O., Beverage, H.H., Moore, J.: Diversity telephone receiving system of RCA communications, inc. In: Proceedings of IRE, pp. 562–584 (1931) 23. Rappaport, T.: Wireless Communications, 2nd edn. Prentice-Hall (2001) 24. Rice, S.: Mathematical analysis of random noise. AT&T Bell Laboratory Technical Journal 23(2), 282–333 (1944) 25. Roh, J.C., Rao, B.D.: Multiple antenna channels with partial channel state information at the transmitter. IEEE Transactions on Wireless Communications 3(2), 677–688 (2004) 26. Roh, J.C., Rao, B.D.: Transmit beamforming in multiple-antenna systems with finite rate feedback: a VQ-based approach. IEEE Transactions on Information Theory 52(3), 1101–1112 (2006) 27. Rustako, Jr., A.J., Amiray, N., Owens, G., Roman, R.: Radio propagation at microwave frequencies for line-of-sight microcellular mobile and personal communications. IEEE Transactions on Vehicular Technology 40(1), 203– 210 (1991) 28. Sanayei, S., Nosratinia, A.: Antenna selection in MIMO systems. IEEE Communications Magazine 42(10), 68–73 (2004) 29. Sanayei, S., Nosratinia, A.: Asymptotic capacity analysis of transmit antenna selection. In: Proc. on IEEE International Symposium on Information Theory (ISIT), pp. 241 (2004) 30. Seidel, S., Rappaport, T., Jain, S., Lord, M., Singh, R.: Path loss, scattering, and multipath delay statistics in four European cities for digital cellular and microcellular radiotelephone. IEEE Transactions on Vehicular Technology 40(4), 721–730 (1991) 31. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27(3), 379–423, 623–656 (1948) 32. Tarokh, V., Jafarkhani, H., Calderbank, A.: Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory 45(5), 1456–1467 (1999) 33. Tarokh, V., Seshadri, N., Calderbank, A.: Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Transactions on Information Theory 44(2), 744–765 (1999) ˙ 34. Telatar, I.E.: Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications 10(6), 585–595 (1999) 35. de Toledo, A.F., Turkmani, A.M.D.: Propagation into and within buildings at 900, 1800, and 2300 MHz. In: Proc. on IEEE Vehicular Technology Conference (VTC), pp. 633– 636 (1992) 36. de Toledo, A.F., Turkmani, A.M.D., Parsons, J.D.: Estimating coverage of radio transmission into and within buildings at 900, 1800, and 2300 MHz. IEEE Personal Communications Magazine 5(2), pp. 40–47 (1998) 37. Tse, D., Viswanath, P.: Fundamentals of Wireless Communication. Cambridge University Press (2005) 38. Xia, P., Giannakis, G.B.: Design and analysis of transmit-beamforming based on limited-rate feedback. IEEE Transactions on Signal Processing 54(5), 1853–1863 (2006)

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39. Yan, Q., Blum, R.: Improved space-time convolutional codes for quasi-static slow fading channels. IEEE Transactions on Wireless Communications 1(4), 563–571 (2002) 40. Zheng, L., Tse, D.N.C.: Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory 49(5), 1073– 1096 (2003) 41. Zhou, S., Giannakis, G.: Optimal transmitter eigen-beamforming and space-time block coding based on channel mean feedback. IEEE Transactions on Signal Processing 50(10), 2599–2613 (2002)

Chapter 3

Two-User Cooperative Transmission Schemes

Cooperative communications refer to systems or techniques that allow users to help transmit each other’s messages to the destination. Most cooperative transmission schemes involve two phases of transmission: a coordination phase, where users exchange their own source data and control messages with each other and/or the destination, and a cooperation phase, where the users cooperatively retransmit their messages to the destination. A basic cooperation system consists of two users transmitting to a common destination, as illustrated in Fig. 3.1. At any instant in time, one user acts as the source while the other user serves as the relay. In the coordination phase (i.e., Phase I), the source user broadcasts its data to both the relay and the destination and, in the cooperation phase (i.e., Phase II), the relay forwards the source’s data (either by itself or by cooperating with the source) to enhance reception at the destination. The two users may interchange their roles as source and relay at different instants in time. To enable such cooperation among users, different relay technology can be employed depending on the relative user location, channel conditions, and transceiver complexity. In this chapter, we introduce some of the basic cooperative relaying techniques such as decode-and-forward (DF) [18], amplify-and-forward (AF) [18], coded cooperation (CC) [10, 12, 14], and compress-and-forward (CF) [9, 17, 20, 21] etc. Moreover, for practical purposes, we also briefly discuss how channel estimation can be performed in cooperative systems to enable coherent detection at the destination.

3.1 Decode-and-Forward Relaying Schemes Decode-and-forward (DF) relaying schemes refer to cases where the relay explicitly decodes the message transmitted by the source and forwards a newly generated signal to the destination, as illustrated in Fig. 3.2. These schemes are also known as regenerative relaying schemes, which have been widely

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_3, © Springer Science+Business Media, LLC 2010

67

68

3 Two-User Cooperative Transmission Schemes

Fig. 3.1 A simple protocol of a two-user cooperative network.

adopted in the literature, including those employed in conventional multihop networks. In this section, we introduce three variants of DF relaying schemes: the basic DF relaying scheme (Basic DF), the selection DF relaying scheme (Selection DF), and the demodulate-and-forward (DeF) relaying scheme. In Basic DF, the relay is assigned to forward the source message in Phase II, given that it has successfully decoded it in Phase I. However, in Selection DF, the source is allowed to retransmit the message by itself in Phase II if the relay is not able to successfully decode the message in Phase I. Moreover, in cases where the relay is not able to perform channel decoding or encoding, either due to limited transceiver capability or lack of knowledge on the channel codebook, one can employ the DeF relaying scheme where the relay demodulates and forwards the message on a symbol-by-symbol basis. These schemes are described in the following.

3.1.1 Basic DF Relaying Scheme In the basic DF relaying scheme, the source first transmits a message to both the relay and the destination in Phase I. Then, if the relay is able to successfully decode the message, it will regenerate the same message and

3.1 Decode-and-Forward Relaying Schemes

69

Fig. 3.2 Illustration of decode-and-forward (DF) relaying schemes.

forward it to the destination in Phase II. Assume that, in each cooperative transmission, an equal amount of time is allocated to Phase I and Phase II. Specifically, let xs = [xs [0], xs [1], . . . , xs [M − 1]] be the length-M code word transmitted by the source in Phase I and let E |xs [m]|2 = 1 for all m. Due to the broadcast nature of the wireless medium, both the relay and the destination will receive a noisy version of the signal, which are given by yr [m] = hs,r Ps xs [m] + wr [m], (3.1) (1) (1) yd [m] = hs,d Ps xs [m] + wd [m], (3.2) respectively, for m = 0, 1, · · · , M − 1. Here, Ps is the source transmission power, hs,r and hs,d are the channel coefficients of the source-to-relay (s-r) and the source-to-destination (s-d) links, respectively, and wr [m] ∼ CN (0, σr2 ) (1) and wd [m] ∼ CN (0, σd2 ) are the additive white Gaussian noise (AWGN) at the relay and the destination, respectively. The channel is assumed to remain constant over the transmission of a codeword but varies independently and identically from block-to-block. With sufficiently long codewords, one can invoke the channel coding theorem and assume that decoding at the relay is successful if and only if the transmission rate is no greater than the capacity of the s-r link, which is given by Cs,r (γs,r ) = log2 (1 + γs,r )

bits per channel use,

(3.3)

where γs,r = Ps |hs,r |2 /σr2 . If the desired average end-to-end rate is R, the codeword xs must be encoded with rate 2R since it will be transmitted twice (i.e, once by the source and once by the relay) throughout the transmission process. In this case, an outage occurs on the s-r link if 2R > Cs,r (γs,r ). When the relay is able to correctly decode the message, i.e., when 2R ≤ Cs,r (γs,r ), it will re-encode the message into a codeword xr using the same

70

3 Two-User Cooperative Transmission Schemes

codebook1 , such that xr = xs , and retransmit it to the destination in Phase II. Thus, the signal received at the destination in Phase II is given by (2) (2) yd [m] = hr,d Pr xs [m] + wd [m], for m = 0, 1, · · · , M − 1, (3.4) where Pr is the relay transmission power, hr,d is the channel coefficient of the (2) relay-to-destination (r-d) link, and wd [m] ∼ CN (0, σd2 ) is the AWGN at the destination in the m-th symbol period of Phase II. In the two-phase cooperation scheme, two copies of the message will be received at the destination — one in Phase I (i.e., (3.2)) and another in Phase II (i.e., (3.4)). When the distance between source and destination is large, the signal received on the s-d link will be negligible and, thus, can be discarded at the destination. However, if both signals have comparable strength, they can be properly combined to enhance detection at the destination. In the following, we shall refer to the former as the case without diversity combining (i.e., Case I) and the latter as the case with diversity combining (i.e., Case II). The outage probability for both cases are analyzed in the following and the optimal power allocation between source and relay is derived under different assumptions on the channel state information (CSI).

Case I: Without Diversity Combining In the case without diversity combining, the destination performs detection based only on the signal received from the relay in Phase II. The scheme is identical to conventional multi-hop (or, in this case, dual-hop) transmissions. In order to successfully transmit a codeword over both s-r and r-d links in this case, the rate of the codeword must be bounded by the capacity of both links, i.e., 2R ≤ min {log2 (1 + γs,r ) , log2 (1 + γr,d )} , (3.5) where γs,r = Ps |hs,r |2 /σr2 and γr,d = Pr |hr,d |2 /σd2 . Hence, the average endto-end achievable rate in Case I (without diversity combining) is given by CBasicDF,I (γs,r , γr,d ) =

1 min {log2 (1 + γs,r ) , log2 (1 + γr,d )} . 2

(3.6)

Hence, an outage occurs when R > CBasicDF,I . Let us consider the Rayleigh fading scenario, where hs,r , hr,d and hs,d are independent circularly symmetric complex Gaussian random variables 2 2 2 with zero mean and variances ηs,r , ηr,d , and ηs,d , respectively. The outage probability can be computed as follows:

1 In this section, we assume that the relay employs the same channel codebook as the source. Information-theoretic studies that may involve different codebooks at the source and the relay will be discussed in Chapter 5.

3.1 Decode-and-Forward Relaying Schemes

71

pout = Pr (min {log2 (1 + γs,r ) , log2 (1 + γr,d )} < 2R) = 1 − Pr (min {log2 (1 + γs,r ) , log2 (1 + γr,d )} ≥ 2R) = 1 − Pr (log2 (1 + γs,r ) ≥ 2R, log2 (1 + γr,d ) ≥ 2R) . Since hs,r and hr,d are assumed to be independent with distributions hs,r ∼ 2 2 CN (0, ηs,r ) and hr,d ∼ CN (0, ηr,d ), respectively, it follows that γs,r and γr,d are exponentially distributed with mean γ s,r 

2 Ps ηs,r σr2

and γ r,d 

2 Pr ηr,d , σd2

respectively. Thus, the outage probability can be further evaluated as * + * + pout = 1 − Pr γs,r ≥ 22R − 1 Pr γr,d ≥ 22R − 1   2R   2R 2 −1 2 −1 exp − . (3.7) = 1 − exp − γ s,r γ r,d Given the total power constraint Ps + Pr ≤ 2P , we can set Ps = 2βP and 2 Pr = 2(1 − β)P , for some β ∈ (0, 1), and let σr2 = σd2 = σw . For example, if β = 1/2, then we have Ps = Pr = P . Then, at high SNR (i.e., when 2 SNR  P/σw  0), the outage probability can be approximated as   1 22R − 1 22R − 1 22R − 1 1 . (3.8) + = + pout ≈ 2 2 γ s,r γ r,d SNR 2βηs,r 2(1 − β)ηr,d This shows that the diversity order in Basic DF without diversity combining is equal to 1. It is worthwhile to remark that, in the case of direct transmission, the outage probability is given by * + pdirect = Pr (log2 (1 + γs,d ) < R) = Pr γs,d < 2R − 1 out   R 2R − 1 1 2 −1 · 2 , ≈ (3.9) = 1 − exp − γ s,d SNR ηs,d 2 2 2 where γs,d = P |hs,d |2 /σw and γ s,d = E[γs,d ] = P ηs,d /σw . By comparing (3.8) with (3.9), one can see that no diversity gain is obtained over direct transmission by employing multi-hop transmission (i.e., Basic DF without diversity combining). Even though this may be the case, multi-hop transmissions may still be useful in practice where the minimum adaptable transmission rate and the maximum transmission power are limited. Although diversity gains cannot be obtained in this case, one can still perform power allocation to improve the coding gains. Specifically, given the total power constraint Ps + Pr ≤ 2P , the optimal power allocation between Ps and Pr can be found by minimizing the outage probability in (3.7). Due to monotonicity of the exponential function, the optimization problem (c.f. [8])

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3 Two-User Cooperative Transmission Schemes

can be formulated as follows: min

Ps ,Pr

1 1 + 2 /σ 2 2 /σ 2 Ps ηs,r P η r r,d r d

subject to Ps + Pr ≤ 2P

and Ps , Pr ≥ 0.

(3.10) (3.11)

By introducing Lagrange multipliers, the optimal power allocation can be derived as Ps = 2P ·

2 2 /σd2 ηr,d ηs,r /σr2 and P = 2P · . r 2 /σ 2 + η 2 /σ 2 2 /σ 2 + η 2 /σ 2 ηs,r ηs,r r r r,d d r,d d

(3.12)

2 Thus, by substituting (3.12) into (3.7) and by assuming that σr2 = σd2 = σw , the minimum outage probability can be expressed as    1 22R − 1 1 pout = 1 − exp − (3.13) + 2 2 /σ 2 P ηs,r ηr,d /σd2 r    1 22R − 1 1 . (3.14) = 1 − exp − + 2 2 SNR ηs,r ηr,d

Notice that, when the optimal power allocation is found by minimizing the outage probability, only the statistics of the channel must be known. However, when instantaneous CSI is available at the source and the relay, one can further derive the optimal power allocation to maximize the dualhop capacity CBasicDF,I (γs,r , γr,d ). In this case, the optimal power allocation is determined by solving the following optimization problem [31]:

   0 1 Ps |hs,r |2 Pr |hr,d |2 min log2 1 + , log 1 + (3.15) max 2 Ps ,Pr 2 σr2 σd2 subject to Ps + Pr ≤ 2P and Ps , Pr ≥ 0.

(3.16)

One can observe that, to maximize the dual-hop capacity given in (3.15), the power constraint Ps + Pr ≤ 2P must be satisfied with equality. Thus, as Ps increases, the first term of (3.15) will increase while the second term decreases, and vice versa as Ps decreases. Since either term inside the minimum of (3.15) cannot be increased without decreasing the other, the objective is maximized when both terms are equal, i.e., when Ps |hs,r |2 Pr |hr,d |2 = . σr2 σd2 The optimal power allocation under instantaneous CSI is given by Ps = 2P ·

|hr,d |2 /σd2 |hs,r |2 /σr2 + |hr,d |2 /σd2

(3.17a)

3.1 Decode-and-Forward Relaying Schemes

73

0

10

−1

Outage Probability

10

−2

10

−3

10

No Cooperation Basic DF I, OPA, Statistical CSI Basic DF I, OPA, Instantaneous CSI 0

5

10

15 20 Transmit SNR (dB)

25

30

Fig. 3.3 Performance comparison of the basic DF relaying schemes in Case I (without diversity combining).

and Pr = 2P ·

|hs,r |2 /σr2 . |hs,r |2 /σr2 + |hr,d |2 /σd2

(3.17b)

2 Furthermore, by assuming that σr2 = σd2 = σw , the resulting dual-hop capacity is instead given by   1 2SNR log2 1 + . 2 1/|hs,r |2 + 1/|hr,d|2

Notice that, in the case of direct transmission, the capacity is given by * + log2 1 + SNR · |hs,d |2 . Let us consider, for example, the case where |hs,d |2 ∝ 1/dα and |hs,r |2 , |hr,d |2 ∝ 1/( d2 )α (i.e., the case where the relay is located in the middle of the source and the destination). By utilizing the approximation log(1 + x) ≈ x, for x  1, one can show that a gain of 2α−1 can be obtained at low SNR when taking the ratio between the capacity of Basic DF (without diversity combining) and that of direct transmission. In Fig. 3.3, we show the outage probabilities versus transmit SNR (i.e., 2 SNR = P/σw ) for the basic DF relaying scheme without diversity combining.

74

3 Two-User Cooperative Transmission Schemes

Here, the rate is set as R = 1 bits/sec/Hertz. Let ds,r , dr,d , and ds,d be the distances between s-r, r-d, and s-d, respectively. The channel coefficients are as−α sumed independent with distribution hs,r = CN (0, d−α s,r ), hr,d = CN (0, dr,d ), −α and hs,d = CN (0, ds,d ), where path loss exponent α is set as 3. In this experiment, we assume that the relay is located in the middle between source and destination, so that ds,d = 1 and ds,r = dr,d = 1/2. Setting ds,d = 1 means that the results are normalized by the performance of the s-d link. The Statistical CSI curve is obtained by employing the optimal power allocation (OPA) in (3.12) and the Instantaneous CSI curve is obtained by the optimal power allocation in (3.17). One can observe that, since no diversity gain is obtained, all three curves decrease at the same rate with respect to SNR. Moreover, the Basic DF scheme without diversity combining may not even be able to outperform direct transmission due to its lack of bandwidth efficiency (when transmitting the same codeword twice in a cooperative transmission period). However, significant power gains can be observed when optimal power allocation is derived under instantaneous CSI.

Case II: With Diversity Combining In the previous case, only the signal transmitted by the relay is utilized for detection at the destination. However, due to the broadcast nature of the wireless medium, the signal transmitted by the source in Phase I will also be received at the destination and, thus, can be combined with the signal received from the relay to increase diversity. This is especially useful when the quality of the s-d link is comparable to that of the r-d link. First, recall that, in the basic DF scheme, the relay is allowed to forward the source’s message in Phase II only if it is able to successfully decode the message in Phase I. If this is the case, the destination will receive two copies of the signal, as given in (3.2) and (3.4), which can be collected into the vector  √     (1) (1) P h [m] yd [m] w d yd [m] = = √ s s,d xs [m] + , (3.18) (2) (2) Pr hr,d yd [m] wd [m] for m = 0, . . . , M −1. The signal model is similar to conventional single-input multiple-output (SIMO) systems and the maximal ratio combiner (MRC) can be employed to maximize the receive SNR at the destination. Specifically, the signals √ received in Phase I and Phase II are multiplied by the weight √ coefficients Ps h∗s,d and Pr h∗r,d , respectively, to obtain √ √ Ps h∗s,d Pr h∗r,d yd [m] * + = Ps |hs,d |2 + Pr |hr,d |2 xs [m] + w ˜d [m],

y˜d [m] =

(3.19)

3.1 Decode-and-Forward Relaying Schemes

75

√ √ (1) (2) where w ˜d [m] = Ps h∗s,d wd [m] + Pr h∗r,d wd [m] ∼ CN (0, σd2 (Ps |hs,d |2 + 2 Pr |hr,d | )) is the effective noise at the output of the MRC. The SNR at the output of the MRC is given by γBasicDF,II =

Ps |hs,d |2 Pr |hr,d |2 + = γs,d + γr,d , 2 σd σd2

(3.20)

where γs,d = Ps |hs,d |2 /σd2 and γr,d = Pr |hr,d |2 /σd2 . Given that the relay successfully decodes the message, the rate achievable in Phase II of the DF scheme (with diversity combining) is given by log2 (1 + γs,d + γr,d ) .

(3.21)

However, in order to have the relay successfully decode the message in Phase I, the rate transmitted by the source must be less than the capacity of the s-r link, i.e., log2 (1 + γs,r ), where γs,r = Ps |hs,r |2 /σr2 . Consequently, the maximum achievable end-to-end rate is given by CBasicDF,II (γ) =

1 min {log2 (1 + γs,r ) , log2 (1 + γs,d + γr,d )} , 2

(3.22)

where γ = (γs,r , γs,d , γr,d ). Therefore, the outage probability of the basic DF relaying scheme in Case II (i.e., with diversity combining) can be computed as   1 pout = Pr min {log2 (1 + γs,r ) , log2 (1 + γs,d + γr,d )} < R 2   1 log2 (1 + γs,r ) < R = Pr 2     1 1 log2 (1 + γs,r ) ≥ R Pr log2 (1 + γs,d + γr,d ) < R + Pr 2 2 + * + * + * 2R 2R = Pr γs,r < 2 − 1 + Pr γs,r ≥ 2 − 1 Pr γs,d +γr,d < 22R − 1 In the Rayleigh fading scenario, the SNR γs,r is exponentially distributed 2 with mean γ s,r = Ps ηs,r /σr2 . Hence, we have pout

  2R   2R * + 2 −1 2 −1 + exp − Pr γs,d + γr,d < 22R − 1 . = 1 − exp − γ s,r γ s,r (3.23)

To further evaluate pout , it is necessary to obtain the distribution of γs,d + γr,d . In general, for any two independent exponential random variables U and V , the CDF of W = U + V can be computed as [18]

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3 Two-User Cooperative Transmission Schemes

Pr (W ≤ w) =

⎧ ( ⎨1 − ⎩

) − μwv v , for μu = μv e + μvμ−μ  wu −μ 1− 1+ w , for μu = μv = μ μ e

− μwu μu μu −μv e 

where μu and μv are expectations of U and V , respectively. Since γs,d and 2 γr,d are independent exponential random variables with mean Ps ηs,d /σd2 and 2 2 Pr ηr,d /σd , respectively, we can utilize the above result to show that * + Pr γs,d + γr,d < 22R − 1 ⎧     ⎨ 1 − γ s,d exp − 22R −1 − γ r,d exp − 22R −1 , if γ s,d = γ r,d γ s,d −γ r,d γ s,d  γ r,d γ r,d s,d  −γ2R   = 2R ⎩ if γ s,d = γ r,d . 1 − 1 + 2 γ −1 exp − 2 γ −1 , s,d

s,d

(3.24) By substituting (3.24) into (3.23), the outage probability can be expressed as ⎧  2R  (  2R  γ s,d 2 −1 ⎪ 1 − exp − exp − 2 −1 ⎪ ⎪ γ s,r γ s,d −γ ⎨ r,d 2R ) γ s,d γ r,d pout = if γ s,d = γ r,d exp − 2 γ −1 , + γ −γ r,d  s,d  r,d ⎪   ⎪ 2R 2R 2R ⎪ ⎩ 1 − 1 + 2 −1 exp − 2 −1 − 2 −1 , if γ s,d = γ r,d . γ γ γ s,d

s,r

s,d

(3.25) At high SNR (i.e., when γ s,d , γ s,r , γ r,d  0), we can adopt the first order Taylor approximation (where e−x ≈ 1 − x for x sufficiently small) to obtain an approximation of the outage probability as ⎧ 22R −1 ⎨ if γ s,d = γ r,d γ s,r ,  2  pout ≈ 2R 2R 2 −1 ) ( 1 ⎩ 2 −1 + + γ 1 , if γ s,r = γ r,d . γ γ γ s,r

s,d

s,r

s,d

By letting Ps = 2βP and Pr = 2(1 − β)P (0 < β ≤ 1) and by assuming that 2 σr2 = σd2 = σw , we can further show that ⎧ 22R −1 1 2 2 ⎨ if βηs,d

= (1 − β)ηr,d 2βη 2 SNR , (3.26) pout ≈ 22R −1 1 s,r * 1 + 2 2 ⎩ 2βη2 SNR + O SNR2 , if βηs,d = (1 − β)ηr,d . s,r

2 where SNR = P/σw . This shows that the diversity order of the basic DF relaying scheme is still equal to 1 even when diversity combining is employed at the destination. This is due to the fact that the relay must first be able to successfully decode the message before the cooperative transmission can take place. Thus, the achievable rate is limited by the capacity of the s-r link. However, this can be improved upon if we allow the destination to decode

3.1 Decode-and-Forward Relaying Schemes

77

the message based only on the signal received from the source, which leads to the so called selection DF relaying scheme discussed in the following section. To further minimize outage probability, we can also derive the optimal power allocation that minimizes the expression in (3.24). However, a closedform of the solution is not easily attainable in this case and, thus, numerical methods must be utilized instead. On the other hand, when instantaneous CSI is available at the transmitters, one can also employ the optimal power allocation to maximize the achievable end-to-end rate given in (3.22). Specifically, given the total power constraint Ps + Pr ≤ 2P , the optimal power allocation can be found by solving the following optimization problem [31]:

   0 1 Ps |hs,r |2 Ps |hs,d |2 Pr |hr,d |2 min log2 1 + , log2 1 + max + Ps ,Pr 2 σr2 σd2 σd2 subject to

Ps + Pr ≤ 2P and Ps , Pr ≥ 0.

Since the achievable rate is maximized when Ps + Pr = 2P , we can substitute Pr with 2P − Ps and omit the factor constant 1/2 to simplify the problem as    0

Ps |hs,r |2 Ps (|hs,d |2 −|hr,d|2 ) 2P |hr,d|2 , log2 1+ . + max min log2 1+ 0≤Ps ≤2P σr2 σd2 σd2 (3.27) When |hs,d |2 ≥ |hr,d |2 , one can observe that both terms inside the minimum operation in (3.27) are monotonically increasing with respect to the power Ps and, thus, the achievable rate is maximized by choosing Ps = 2P and Pr = 0. This implies that, when the quality of the s-d link is better than that of the r-d link, one need not rely on help from the relay. On the other hand, when |hs,d |2 < |hr,d |2 , the first term inside the minimum operation increases with Ps while the second term decreases with Ps . In this case, the objective is maximized when both terms are equal and, thus, the optimal power allocation is given by Ps = 2P · and Pr = 2P ·

|hs,r |2 /σr2

|hr,d |2 /σd2 + (|hr,d |2 − |hs,d |2 )/σd2

|hs,r |2 /σr2 − |hs,d |2 /σd2 . 2 |hs,r | /σr2 + (|hr,d |2 − |hs,d |2 )/σd2

(3.28a)

(3.28b)

By comparing the results of Case I and Case II, i.e., (3.17) and (3.28), one can observe that more power is allocated to the source when diversity combining is employed at the destination. The reason is that, when diversity combining is employed, the signal transmitted by the source will be utilized for detection at both the relay and the destination and, thereby, is more essential to achieving a higher performance in the cooperative transmission. In Fig. 3.4, we compare the outage probabilities of the basic DF relaying strategies with and without diversity combining at the destination. The

78

3 Two-User Cooperative Transmission Schemes

0

10

−1

Outage Probability

10

−2

10

No Cooperation Basic DF I, OPA, Statistical CSI Basic DF I, OPA, Instantaneous CSI Basic DF II, OPA, Statistical CSI Basic DF II, OPA, Instantaneous CSI

−3

10

0

6

12 Transmit SNR (dB)

18

24

Fig. 3.4 Performance comparison of basic DF relaying schemes.

experiments are performed with optimal power allocations (OPA) based on instantaneous CSI and statistical CSI (obtained by numerically minimizing outage probability). The simulation scenario is the same as that in Fig. 3.3. We can see that the diversity order is the same for all schemes but a power gain is observed when optimal power allocation based on instantaneous CSI is employed and/or when diversity combining is performed at the destination.

3.1.2 Selection DF Relaying Scheme In basic DF relaying schemes, the outage performance is limited by the quality of the s-r link since the relay is required to successfully decode the source’s message. However, the diversity order can be improved if the strategy does not insist on having the relay participate in the cooperative transmission of Phase II. Specifically, in the selection DF relaying scheme (as proposed in [18]), the source can choose to retransmit the message itself in Phase II if the relay was not able to successfully decode the message in Phase I. We assume that the source can obtain knowledge of the instantaneous CSI on the s-r link and, thus, is able to infer whether or not the relay has successfully decoded the message.

3.1 Decode-and-Forward Relaying Schemes

(a) Phase I

79

(b) Phase II

Fig. 3.5 Illustration of the selection DF relaying scheme.

The selection DF relaying scheme takes on two phases of transmission, as illustrated in Fig. 3.5. In Phase I, the source transmits a length-M codeword xs = [xs [0], . . . , xs [M − 1]] to both the relay and the destination, similar to the basic DF scheme. The received signal at the relay and the destination are given, respectively, by (3.1) and (3.2). Similarly, to achieve the end-to-end rate R, the source must encode the message with rate 2R and, thus, the relay is able to successfully decode the message only if 2R < log2 (1 + γs,r ). If the relay is able to successfully decode the message, it will regenerate the same codeword xr = xs and forward it to the destination. Otherwise, the source will retransmit the codeword xs in Phase II, without help from the relay. The signal received at the destination in Phase II is given by , √ (2) hr,d Pr xs [m] + wd [m], if γs,r ≥ 22R − 1 (2) yd [m] = (3.29) √ (2) hs,d Pr xs [m] + wd [m], if γs,r < 22R − 1 (2)

where γs,r  Ps |hs,r |2 /σr2 is the SNR on the s-r link and wd [m] ∼ CN (0, σd2 ) is the AWGN at the destination in Phase II. Notice that the source also transmits with power Pr in Phase II so that the same total power Ps +Pr = 2P is utilized in both cases. It is worthwhile to remark that, here, we are utilizing only the simple repetition code where the codeword transmitted by either the source or the relay in Phase II is a repetition of the codeword transmitted in Phase I. In general, more powerful codes can be employed (see Chapter 5) but the repetition code is sufficient to achieve full diversity order of 2 under selection DF relaying. Suppose that the source or relay’s address is appended to the message in Phase II so that the destination can determine the identity of the transmitter and impose appropriate weighting coefficients in the MRC. Then, the effective SNR at the output of the MRC can be obtained as , (1) γs,d + γr,d , if γs,r ≥ 22R − 1, γSDF  (3.30) (1) (2) γs,d + γs,d , if γs,r < 22R − 1,

80

3 Two-User Cooperative Transmission Schemes (1)

(2)

where γs,d  Ps |hs,d |2 /σd2 , γs,d  Pr |hs,d |2 /σd2 are the SNRs on the s-d link in Phases I and II, respectively, and γr,d  Pr |hr,d |2 /σd2 is the SNR on the r-d link. Hence, the achievable end-to-end rate of the selection DF scheme is given by ⎧   ⎨ 1 log2 1 + γ (1) + γr,d , if γs,r ≥ 22R − 1, s,d 2   CSDF (γ) = (3.31) ⎩ 1 log2 1 + γ (1) + γ (2) , if γs,r < 22R − 1, s,d s,d 2 (1)

(2)

where γ = (γs,r , γr,d , γs,d , γs,d ). Under Rayleigh fading assumptions, the outage probability of the selection DF relaying scheme can be computed as     * + 1 (1) 2R pout = Pr γs,r ≥ 2 − 1 Pr log2 1 + γs,d + γr,d < R 2     + * 1 (1) (2) 2R log2 1 + γs,d + γs,d < R , + Pr γs,r < 2 − 1 Pr 2   * + (1) = Pr γs,r ≥ 22R − 1 Pr γs,d + γr,d < 22R − 1  * +  (1) (2) + Pr γs,r < 22R − 1 Pr γs,d + γs,d < 22R − 1 . (3.32) 2 and Since |hs,r |2 and |hs,d |2 are exponential random variables with mean ηs,r 2 ηs,d , respectively, we have

 2R−1  * + 2 Pr γs,r < 22R − 1 = 1 − exp − , γ s,r

(3.33)

and     22R − 1 (1) (2) Pr γs,d + γs,d < 22R − 1 = Pr |hs,d |2 < (Ps + Pr )/σd2   22R − 1 , = 1 − exp − (1) (2) γ s,d + γ s,d (1)

(2)

(3.34) (1)

2 2 where γ s,d = Ps ηs,d /σd2 and γ s,d = Pr ηs,d /σd2 . Moreover, since γs,d and 2 γr,d are independent exponential random variables with mean Ps ηs,d /σd2 and (1)

2 /σd2 , the probability Pr(γs,d + γr,d < 22R − 1) can also be derived usPr ηr,d ing the results in (3.24). By substituting these results into (3.32), the outage probability can be further evaluated as

3.1 Decode-and-Forward Relaying Schemes

pout

81

⎧     γ (1) exp−c/γ (1)  γ exp −c/γ ⎪ ( s,d s,d r,d r,d ) c ⎪ ⎪ 1 − exp − + (1) (1) ⎪ γ s,r γ s,d −γ r,d γ r,d −γ s,d ⎪ ⎪ (  ⎪  ) ⎪ ⎪ (1) ⎪ ⎨ 1 − exp − γ c − exp − (1) c (2) , if γ s,d =

γ r,d s,r γ s,d +γ s,d     = ⎪ c c ⎪ ⎪ exp − γ c − (1) 1 − 1 + (1) ⎪ s,r γ s,d γ s,d ⎪ ⎪ (  ⎪  ) ⎪ ⎪ (1) ⎪ ⎩ 1 − exp − γ c − exp − (1) c (2) , if γ s,d = γ r,d γ s,d +γ s,d

s,r

where c = 22R − 1. Assume that Ps = 2βP , Pr = 2(1 − β)P , and σr2 = σd2 = 2 σw . By first order Taylor approximation, the outage probability at high SNR can be approximated as ⎧ (1) (22R −1)2 1 ⎨ if γ s,d = γ r,d , 2 η2 SNR2 4βηs,r s,d ( ) pout ≈ (22R −1)2 1+β , (3.35) (1) 1 1 ⎩ 2 2 η2 + η2 SNR2 if γ s,d = γ r,d 4β η s,d

s,r

s,d

2 where SNR = P/σw . Hence, the diversity order of the selection DF scheme is equal to 2. This is achievable since the transmission is no longer contingent upon the successful decoding at the relay and the source message can be transmitted opportunistically through one of two independent fading paths, i.e., the direct path or the relay path. In Fig. 3.6, we compare the outage probability of Selection DF scheme with that of Basic DF with diversity combining. Here, we assume that the relay is located in the middle of the source and the destination, similar to that in Figs. 3.3 and 3.4. We can see that Selection DF indeed achieves diversity order 2 and, thus, the outage probability decreases twice as fast as that of Basic DF schemes. In Fig. 3.7, we show the outage probabilities versus the distance ds,r = 1 − dr,d for the various schemes described above 2 with SNR = P/σw = 20 dB. The variances of channel coefficients are set as −α 2 −α 2 2 ηs,r = ds,r , ηr,d = d−α r,d , and ηs,d = ds,d , and the path loss exponent equals to 3. We can see that the outage probability of the basic DF scheme increases as the distance between the source and the relay increases. This is expected since the performance of the basic DF scheme is limited by the quality of the s-r link. However, in the selection DF scheme, the diversity gain allows the outage probability to remain at a low level even when the quality of the s-r link deteriorates. Moreover, we can observe that the outage probability is minimized when ds,r ≈ 0.5.

3.1.3 Demodulate-and-Forward Relaying Scheme In the DF schemes described previously, the relay forwards the source’s message only if it is able to successfully decode the message in Phase I. However,

82

3 Two-User Cooperative Transmission Schemes

0

10

−1

Outage Probability

10

−2

10

−3

10

−4

10

No Cooperation Basic DF II, OPA, Statistical CSI Basic DF II, OPA, Instantaneous CSI Selection DF 0

6

12 Transmit SNR (dB)

18

24

Fig. 3.6 Performance comparison of selection DF relaying and basic DF relaying schemes with diversity combining when the relay node is located in the middle of the source and the destination nodes.

in many applications, channel decoding may not be desirable at the relays either due to limited transceiver capabilities or due to lack of knowledge of the channel codebook. In this case, the signals transmitted by the source can only be detected or demodulated on a symbol-by-symbol basis. With no error protection, the forwarded symbols may be incorrect and, thus, the error probability must be taken into consideration in the receiver design at the destination. The studies of DF in uncoded systems will be referred to as demodulate-and-forward (DeF) relaying schemes [2, 27, 28]. In the DeF relaying scheme, cooperation also takes on two phases of transmission. In Phase I, the source transmits a block of symbols xs = [xs [0], . . . , xs [M − 1]]T to both the relay and the destination, where the received signals are given by (3.1) and (3.2), respectively. Suppose that binary phase shift keying (BPSK) modulation is employed at the transmitters such that xs [m] ∈ {−1, +1}, for all m. The maximum likelihood (ML) detector at the relay can be expressed as * . /+ x ˆs [m] = sgn  h∗s,r yr [m] , (3.36) where

3.1 Decode-and-Forward Relaying Schemes

83

No Cooperation Basic DF II, OPA, Statistical CSI Basic DF II, OPA, Instantaneous CSI Selection DF −2

Outage Probability

10

−3

10

−4

10

0.1

0.2

0.3

0.4

0.5 ds,r

0.6

0.7

0.8

0.9

Fig. 3.7 Performance comparison of selection DF relaying and basic DF relaying schemes with diversity combining in terms of the distance between the source and relay node.

⎧ ⎪ ⎨1, sgn(u) = 0, ⎪ ⎩ −1,

if u > 0, if u = 0, if u > 0,

is the sign function. Thus, the bit error rate (BER) is given by + *εr (γs,r )  Pr (ˆ xs [m] = xs [m]) = Q 2γs,r ,

(3.37)

where γs,r = Ps |hs,r |2 /σr2 is the SNR on the s-r link and the Q-function defined by  ∞ 2 1 Q(u) = √ e−t /2 dt, (3.38) 2π u i.e., the complementary CDF of a standard normal random variable. In Phase II, the relay forwards the detected symbol to the destination, regardless of whether or not it was detected correctly. Hence, the signal received at the destination can be given by (2) (2) yd [m] = hr,d Pr xr [m] + wd [m], (3.39) where xr [m] = x ˆs [m], for m = 0, . . . , M − 1. Given knowledge of the error probability at the relay, the ML detector can also be employed at the destination, combining the signals received in both Phase I and Phase II. Since

84

3 Two-User Cooperative Transmission Schemes

the detection is performed on a symbol-by-symbol basis, we shall omit the time index m in the following discussions. Let ⎛  2 ⎞ √   (1)   − h P x  ⎟ y s,d s s  d 1 ⎜ (1)  fY (1) yd hs,d , xs = exp ⎝− ⎠ 2 2 d πσd σd (1)

be the conditional PDF of yd xs and let

fY (2) d

given the channel hs,d and the source symbol

⎛  2 ⎞ √   (2) − h P x  ⎟ y r,d r s  + d 1 ⎜ (2)  yd hs,r ,hr,d , xs = [1 − εr (γs,r )] 2 exp ⎝− ⎠ 2 πσd σd

*

⎛  2 ⎞ √   (2) + h P x r,d r s ⎟ 1 ⎜ yd + εr (γs,r ) 2 exp ⎝− ⎠ πσd σd2

(3.40)

(2)

be the conditional PDF of yd given the channels hs,r , hr,d and the source symbol xs . The first and second terms correspond to the case that a correct decision is made at the relay and the opposite case where an incorrect decision (1) (2) is made. Given the received signals in both phases, i.e., yd and yd , the ML detector can be derived as     (1)  (2)  x ˆs = arg max fY (1) yd hs,d , x fY (2) yd hs,r , hr,d , x x∈{−1,+1}

d



= arg max ln fY (1) x∈{−1,+1}

d

d

   (2)  (1)  yd hs,d , x + ln fY (2) yd hs,r , hr,d , x d

4 5 (  4 5 (1) (2) = arg max 2Re ωs,d yd x + ln (1 − εr (γs,r )) exp 2Re ωr,d yd x x∈{−1,+1}

 4 5) (2) +εr (γs,r ) exp −2Re ωr,d yd x ,

(3.41)

√ √ where ωs,d = h∗s,d Ps /σd2 and ωr,d = h∗r,d Pr /σd2 are the coefficients of the combiner at the destination. When the relay is noiseless such that εr (γs,r ) = 0, for all γs,r , the ML detection reduces to the MRC combiner adopted in (3.19). However, for a larger constellation size, the conditional PDF in (3.40) and the ML detector in (3.41) becomes prohibitively complex. Moreover, due to possible error at the relay, the average symbol error probability and, thus, the diversity order of the uncoded DeF scheme can not be easily evaluated. In the following, we introduce the suboptimal cooperative MRC detector proposed in [28] and show how it can achieve full diversity even with the suboptimal design. Since full diversity is achievable by a sub-

3.1 Decode-and-Forward Relaying Schemes

85

optimal receiver, it must also be achievable with the ML detector as well. Suboptimal Cooperative MRC (C-MRC) Detector [28] The main complexity of the ML detector is the need to consider all possible combinations of errors occurring at the relay, e.g., in (3.40) and (3.41). To simplify the computations, one can consider a suboptimal scheme where the ML detector at the destination is aimed at detecting the relayed symbol xr (instead of the actual source symbol xs ), regardless of whether or not the detection at the relay is correct. In this case, the error probability of the relay link (i.e., s-r-d link) will be given by pˆe,relay (γs,r , γr,d ) =εr (γs,r ) [1 − εd (γr,d )] + [1 − εr (γs,r )] εd (γr,d )   =Q( 2γs,r ) 1 − Q( 2γr,d ) + 1 − Q( 2γs,r ) Q( 2γr,d ). If the relay link is viewed as an effective single-hop AWGN channel, an effective SNR for the relay link can be defined as γeff 

2 1  −1 Q (ˆ pe,relay ) , 2

(3.42)

which is obtained by inverting the relation in (3.37) with the BER of the relay path, i.e., pˆe,relay (γs,r , γr,d ). Intuitively, the signal received from the relay in Phase II can viewed as an effective AWGN channel with signal model given by (2) (2) (2) yd [m] = hr,d Pr xr [m] + wd [m] = heff Ps xs [m] + w ˜d [m], √ √ (2) ˜d [m] where heff = Pr hr,d / Ps is the effective s-d channel in Phase II and w is the effective noise that incorporates the detection error at the relay. The noise variance is given by σ ˜d2 = Ps |heff |2 /γeff = Pr |hr,d |2 /γeff . It has been shown in [28] that 3.24 γmin − < γeff ≤ γmin , (3.43) ν where γmin  min{γs,r , γr,d } and ν = 2 (in the case of BPSK). Hence, γeff can be closely approximated by γmin at high SNR. Taking into consideration the relay errors, the C-MRC receiver [28] combines the signals in Phase I and Phase II with the combiner coefficients √ √ √ ∗ Ps h∗s,d Ps h∗eff γeff Pr hr,d ωs,d = and ω = = . eff σd2 σ ˜d2 γr,d σd2 Thus, the signal at the C-MRC output can be expressed as

86

3 Two-User Cooperative Transmission Schemes (1)

(2)

yd = ωs,d yd + ωeff yd ,* √ + √ (1) (2) ωs,d hs,d Ps + ωeff hr,d Pr xs + ωs,d wd + ωeff wd , = * √ √ + (1) (2) ωs,d hs,d Ps − ωeff hr,d Pr xs + ωs,d wd + ωeff wd ,

if x ˆr = xs , if x ˆr = −xs .

The ML detector based on the combiner output is given by   -  2  x ˆs = arg max yd − ωs,d hs,d Ps + ωeff heff Ps x . x∈{−1,+1}

Since BPSK symbols are real-valued, it suffices to take into consideration only the real part of the combiner output, i.e., Re{yd }, whose noise variance is given by σ 2 = (|ws,d |2 + |weff |2 )σd2 /2. The BER of the C-MRC receiver can be computed as pe (γs,r , γr,d , γs,d )  = [1 − εr (γs,r )]

√ +2  yd + (ωs,d hs,d + ωeff heff ) Ps √ dy exp − 2σ 2 2πσ 2 0  * √ +2   ∞ yd + (ωs,d hs,d − ωeff heff ) Ps 1 √ dy + εr (γs,r ) exp − 2σ 2 2πσ 2 0   ' '  2(γs,d + γeff )2 2(γs,d − γeff )2 = 1 − Q( 2γs,r ) Q +Q( 2γs,r )Q 2 /γ 2 /γ γs,d +γeff γs,d +γeff r,d r,d ∞

1

 *

(3.44) To derive the achievable diversity order, let us first bound the first term in (3.44) as  '    2(γs,d + γeff )2 ≤Q 1 − Q( 2γs,r ) Q 2(γs,d + γeff ) , 2 /γ γs,d +γeff r,d which follows from the fact that γeff ≤ γr,d . By utilizing the lower bound 2 in (3.43) and by applying the Chernoff bound (where Q(x) ≤ 12 e−x /2 ), the average of the first term over γs,r , γr,d , and γs,d can be further bounded as    2(γs,d + γeff ) Eγs,r ,γr,d ,γs,d Q 1 Eγ [exp (−γs,d )] Eγs,r ,γr,d [exp (− min{γs,r , γr,d })] e1.62 2 s,d * + e1.62 γ s,r + γ r,d +* +. = * 2 1 + γ s,d γ s,r + γ r,d + γ s,r γ r,d

≤

Let us consider the case where Ps = 2βP , Pr = 2(1 − β)P , and σr2 = σd2 = 2 With SNR = P/σw , the right-hand-side can be approximated by k1 /SNR2 ,

2 σw .

3.2 Amplify-and-Forward Relaying Schemes

87

as SNR → ∞, for some constant k1 . Similarly, by applying the Chernoff bound on the second term, the average of the second term in (3.44) can also be bounded as   ' 2(γs,d − γmin )2 Eγs,r ,γr,d ,γs,d Q( 2γs,r )Q 2 /γ γs,d +γeff r,d     (γs,d − γeff )2 1 1 exp(−γs,r ) exp − 1γs,d ≥γmin ≤ Eγs,r ,γr,d,γs,d 2 /γ 2 2 γs,d +γeff r,d   1 exp(−γs,r )1γs,d log2 (1 + γs,d ), the destination will broadcast a NACK (i.e., negative acknowledge) message to inform both the source and the relay of this failure. Only in this case will the relay participate in the cooperative transmission and forward a scaled version of its received signal to the destination in Phase II. The signals received at the destination in both phases are given similarly by (3.46) and (3.49). The effective SNR at the output of the MRC is given by (3.63). In this case, the transmission rate is equal to R, since two phases of transmission are utilized, and decoding at the destination is successful only if   1 γs,r γr,d R ≤ log2 1 + γs,d + . 2 γs,r + γr,d + 1 Clearly, the transmission rate of the incremental relaying scheme varies depending on the quality of the s-d link. If γs,d ≥ 22R − 1, the transmission rate will be as high as 2R but, if γs,d < 22R − 1, the rate will be only equal to R. Hence, the average transmission rate is computed by * + * + R = 2R · Pr γs,d ≥ 22R − 1 + R · Pr γs,d < 22R − 1   2R 2 −1 , (3.79) = R + R exp − γ s,d 2 /σd2 . Notice that the average rate R is greater than R, where γ s,d  Ps ηs,d but can approach 2R as the SNR γ s,d of the direct link increases. Hence, the bandwidth efficiency is improved compared to previous relaying schemes. In the incremental relaying scheme, an outage occurs only when both the direct transmission in Phase I and the cooperative transmission in Phase II fail simultaneously. Therefore, the outage probability is given by   γs,r γr,d 2R 2R pout = Pr γs,d < 2 − 1, γs,d + R while the remaining N2 = N − N1 symbols, i.e., x(2) = [x[N1 ], . . . , x[N1 + N2 − 1]]T , contains additional parity symbols for the encoded message. Although the message can be decoded alone by the first portion of the codeword x(1) , the combination of x(1) and x(2) together form a more powerful rate-R codeword. In Phase I, the source transmits the first N1 symbols of the codeword, i.e., x(1) , and the signal received at the relay and the destination are given by yr [m] = hs,r Ps x[m] + wr [m], (1) (1) yd [m] = hs,d Ps x[m] + wd [m], for m = 0, . . . , N1 − 1. Let us define κ = N1 /N such that the rate in Phase I is R1 = R/κ. If the relay is able to successfully decode the message, it will generate the remaining N2 symbols of the codeword, i.e., x(2) , and transmit it to the destination in Phase II. If not, the source will transmit the remaining N2 symbols by itself. Notice that outage occurs on the s-r link when log2 (1 + γs,r ) < R/κ. Similar to the selection DF relaying scheme, we assume that the source has knowledge of the s-r channel and, thus, is able to infer whether or not the relay successfully decodes the message. The signal received at the destination in Phase II can be expressed as, , √ (2) hr,d Pr x[N1 + m] + wd [m], if γs,r ≥ 2R/κ − 1, (2) yd [m] = √ (2) hs,d Pr x[N1 + m] + wd [m], otherwise, for m = 0, . . . , N2 − 1, where we assume that both source and relay use power Pr in Phase II. The destination decodes the message based on the concatenation of signals received in both phases, i.e.,

104

3 Two-User Cooperative Transmission Schemes

(a) Phase I

(b) Phase II

Fig. 3.16 Illustration of basic coded cooperation with a single relay.

)T ( (1) (2) yd = (yd )T (yd )T , (1)

(1)

(1)

(2)

(2)

(2)

where yd = [yd [0], . . . , yd [N1 − 1]]T and yd = [yd [0], . . . , yd [N2 − 1]]T . If the signal in Phase II is received from the relay, the achievable rate is given by (a)

CCC (γs,d , γr,d ) = κ log2 (1 + γs,d ) + (1 − κ) log2 (1 + γr,d ) . If the signals in both phases are received from the source, the achievable rate is given by (b) CCC (γs,d ) = log2 (1 + γs,d ) . Different from schemes based on repetition codes, the rate achievable with coded cooperation is not decreased by 1/2. Since N2 out of N total symbols are transmitted by the relay, the ratio 1 − κ characterizes the relay’s level of participation in the cooperative transmission. It is worthwhile to remark that coded cooperation can be achieved with a wide variety of channel codes, including block codes, convolutional codes, or the combination of the two. The symbols in the two portions of the codeword can be partitioned through puncturing, product codes, or other forms of concatenation Examples of coded cooperation using rate-compatible punctured convolutional (RCPC) codes are given in [10,11] and those using turbo codes are given in [14]. The repetition-based DF schemes can be considered as a special case of coded cooperation with N1 = N2 = M and xs = x(1) = x(2) = xr . To evaluate the overall outage probability, we first derive the conditional outage probabilities for two separate cases, i.e., Case (a), where the relay successfully decodes the message, and Case (b), where the relay does not successfully decode the message. In Case (a), the relay will forward the second portion of the codeword in Phase II and the destination will decode based on the concatenation of signals received in both phases. Let Os,r  {log2 (1 + γs,r ) < R/κ}

3.3 Coded Cooperation

105

c be the event that outage occurs on the s-r link and let Os,r be the complementary event of Os,r . The conditional outage probability in Case (a) can be derived as follows:  c + * (a) pout = Pr κ log2 (1 + γs,d ) + (1 − κ) log2 (1 + γr,d ) < R  Os,r   κ (1−κ) = Pr (1 + γs,d ) (1 + γr,d ) (3.81) < 2R .

The second equality follows from the fact that γs,d and γr,d are independent of γs,r . In Case (b), the source transmits the second portion of the codeword itself since the relay was not able to decode it successfully. The conditional outage probability in this case is given by * + (b) pout = Pr (log2 (1 + γs,d ) < R | Os,r ) = Pr γs,d < 2R − 1 .

(3.82)

By taking an average over the two cases, the average outage probability can be computed as     (a) (b) pout = Pr γs,r ≥ 2R/κ − 1 pout + Pr γs,r < 2R/κ − 1 pout (3.83)     κ (1−κ) = Pr γs,r ≥ 2R/κ − 1 Pr (1 + γs,d ) (1 + γr,d ) < 2R   * + + Pr γs,r < 2R/κ − 1 Pr γs,d < 2R − 1 . (3.84) Let us define the event 4 5 κ (1−κ) A  (1 + γs,d ) (1 + γr,d ) < 2R 0

2R/(1−κ) − 1  a(γs,d ) . = γr,d < (1 + γs,d )κ/(1−κ) For the event A to occur, it must be the case that (1+γs,d )κ/(1−κ) < 2R/(1−κ) (or, equivalently, γs,d < 2R/κ − 1) since γr,d is strictly positive. Hence, under Rayleigh fading assumptions, we have

106

3 Two-User Cooperative Transmission Schemes

 κ (1−κ) Pr (1 + γs,d ) (1 + γr,d ) < 2R = Eγs,d ,γr,d [1A ]      2R/κ −1 a(x) 1 1 x y dydx exp − exp − = γ s,d γ s,d γ r,d γ r,d 0 0      2R/κ −1 1 a(x) x = 1 − exp − dx exp − γ s,d γ s,d γ r,d 0   R/κ   2R/κ −1   −1 1 2 x a(x) = 1 − exp − − dx, exp − − γ s,d γ s,d γ s,d γ r,d 0 78 9 6 

Ψ(γ s,d ,γ r,d ,R,κ)

(3.85) where the integral above is defined as Ψ(γ s,d , γ r,d , R, κ). By substituting (3.85) into (3.84), the average outage probability can be further evaluated as   R/κ   R/κ  −1 −1 2 2 pout = exp − 1 − exp − − Ψ(γ s,d , γ r,d , R, κ) γ s,r γ s,d    R    R/κ −1 2 −1 2 1 − exp − . (3.86) + 1 − exp − γ s,r γ s,d 2 Suppose for notational simplicity that Ps = Pr = P and σr2 = σd2 = σw . 2 At high SNR, i.e., when SNR = P/σw  0, the outage probability can be approximated by [12] *  +* +   2R − 1 2R/κ − 1 1 1 Λ (R, κ) +O pout ≈ + 2 2 2 η2 ηs,d ηs,d ηr,d SNR2 SNR3 s,r

where  R/(1−κ)  R/κ 1−κ 2 − 1−2κ 2 +1, κ = 12 , κ = 12 . R ln 2 · 22R+1 − 22R +1,

, Λ (R, κ) =

κ 1−2κ



(3.87)

This shows that coded cooperation also is able to obtain full diversity while achieving higher bandwidth efficiency.

3.3.2 User Multiplexing for Coded Cooperation In the cooperation schemes based on DF, AF, and CC, we assumed that, at any time instant (or in any single channel), only one user serves as the source while the other user servers as the relay that forwards the source’s message to

3.3 Coded Cooperation

107

frequency

Phase I Phase II

User 1 tx

User 2 relays

User 2 tx

User 1 relays

User 1 tx

User 2 relays

time

frequency

(a) TDMA Phase I Phase II

User 2 tx

User 1 relays

User 2 tx

User 1 tx

User 2 relays

User 1 tx

time (b) FDMA Fig. 3.17 Illustration of multiplexing schemes in pairwise cooperative systems.

the destination. However, in a pair-wise cooperative system, both users may have their own data to transmit and, thus, their transmissions must be multiplexed in either time, frequency, or code-space (e.g., using TDMA, FDMA, or CDMA schemes) to allow for interference-free access of the cooperative channel. For example, in TDMA, the two users can take turns in playing the role of the source, as shown in Fig. 3.17(a); in FDMA, the two users can interchange their roles on two different frequency channels, as shown in Fig. 3.17(b). Since each channel can be treated independently, the discussions provided in previous sections, thus, focus only on the relay operations in a single channel. However, joint consideration of the operations in both channels may yield simplified cooperation protocols. In the following, we describe a user multiplexing scheme proposed in [12] that exploits this concept in CC systems. Consider a system that consists of two users, e.g., user 1 and user 2, that cooperate in transmitting both of their messages to the destination. In Phase I, the users’ messages are transmitted through orthogonal channels and are received simultaneously by each other as well as the destination. Suppose that cyclic redundancy check (CRC) codes are applied on the messages so that each user can determine whether or not the messages received from its partner is decoded correctly. If a user succeeds in decoding the message, the user will forward the second portion of its partner’s codeword to the destination; if

108

3 Two-User Cooperative Transmission Schemes

(a) Case (a)

(c) Case (c)

(b) Case (b)

(d) Case (d)

Fig. 3.18 Illustration of the four signal transmission cases in two-user transmission coded cooperation.

not, the user will instead transmit the second portion of its own codeword. The difference with the original CC scheme is that, in this case, whether or not the source user transmits part of its own codeword in Phase II, depends only on whether or not it successfully decodes its partner’s message in Phase I. Therefore, knowledge of the s-r channel or feedback from the relay is no longer required and, thus, the protocol is easier to implement in practice. Let hi,j be the channel coefficient of the link from user i to user j. Suppose that reciprocity is not satisfied on the inter-user channel such that h1,2 and h2,1 are independent. Moreover, for simplicity, assume that both users 2 transmit with the same power P and that σr2 = σd2 = σw . In Rayleigh fading scenarios, the channel coefficients h1,2 , h2,1 , h1,d , and h2,d are assumed to be 2 2 2 2 complex Gaussian with mean 0 and variances η1,2 , η2,1 , η1,d , and η2,d , respectively. Four possible cases may occur throughout the transmission process, as illustrated in Fig. 3.18. In Case (a), both users successfully decode each other’s message and transmit additional parity for the other user in Phase II.

3.3 Coded Cooperation

109

In Case (b), neither user succeeds in decoding the other user’s message and, thus, transmits the second portion of its own codewords. In Case (c), user 2 successfully decodes user 1 but user 1 does not successfully decode user 2. As a result, no user will transmit the second portion of user 2’s codeword. Case (d) is the same as Case (c) but with the role of user 1 and user 2 reversed. Let us define 4 5 O1,2  {log2 (1 + γ1,2 ) < R/κ} = γ1,2 < 2R/κ − 1 as the event that outage occurs on the channel from user 1 to user 2, where 2 γ1,2  P |h1,2 |2 /σw , and define 4 5 O2,1  {log2 (1 + γ2,1 ) < R/κ} = γ2,1 < 2R/κ − 1 as the event that outage occurs on the channel from user 2 to user 1, where 2 γ2,1  P |h2,1 |2 /σw . The outage probabilities for these cases are evaluated separately in the following. Case (a): In Case (a), both users are able to successfully decode their partner’s message in Phase I, which occurs with probability qa = Pr (O1c ∩ O2c )     = Pr γ1,2 ≥ 2R/κ − 1 · Pr γ1,2 ≥ 2R/κ − 1   R/κ   R/κ −1 −1 2 2 exp − , = exp − γ 1,2 γ 2,1

(3.88)

2 2 where γ i,j = E[γi,j ] = P ηi,j /σw , for i, j ∈ {1, 2}. In this case, each user will cooperate by transmitting the second portion of its partner’s codeword in Phase II. Therefore, similar to (3.85), the conditional outage probabilities for users 1 and 2 are given respectively by (a)

pout,1 = Pr (κ log2 (1 + γ1,d ) + (1 − κ) log2 (1 + γ2,d ) < R)   κ (1−κ) = Pr (1 + γ1,d ) (1 + γ2,d ) < 2R   R/κ −1 2 − Ψ(γ 1,d , γ 2,d , R, κ) =1 − exp − γ 1,d and (a)

pout,2 = Pr (κ log2 (1 + γ2,d ) + (1 − κ) log2 (1 + γ1,d ) < R)   R/κ −1 2 − Ψ(γ 2,d , γ 1,d , R, κ), =1 − exp − γ 2,d

110

3 Two-User Cooperative Transmission Schemes

2 2 2 where γi,d  P |hi,d |2 /σw and γ i,d = E[γi,d ] = P ηi,d /σw , for i = 1, 2.

Case (b): In Case (b), both users fail to decode their partner’s message in Phase I, which occurs with probability qb = Pr(O1,2 ∩ O2,1 )   R/κ    R/κ  −1 −1 2 2 = 1 − exp − 1 − exp − . γ 1,2 γ 2,1 Since neither of the two users successfully decode their partners’ messages, they both transmit the second portion of their own codewords in Phase II. The conditional outage probabilities for user 1 and user 2 are given respectively by   R 2 −1 (b) pout,1 = Pr (log2 (1 + γ1,d ) < R) = 1 − exp − γ 1,d and (b) pout,2

  R 2 −1 . = Pr (log2 (1 + P γ2,d ) < R) = 1 − exp − γ 2,d

Case (c): In Case (c), the message transmitted by user 1 is successfully decoded by user 2 in Phase I, but user 2’s message is not successfully decoded at user 1. The probability that this event occurs is given by   R/κ   R/κ −1 −1 2 2 c qc = Pr(O1,2 ∩ O2,1 ) = exp − 1 − exp − . γ 1,2 γ 2,1 Then, in Phase II, both users transmit simultaneously the second portion of user 1’s codeword in their respective channels. The second portion of user 2’s codeword is not transmitted by any user in Phase II and, thus, decoding of it’s message at the destination can be performed based only on the signal received in Phase I. Following similar derivations as in (3.85), the conditional outage probability for user 1 can be computed as (c)

pout,1 = Pr (κ log2 (1 + γ1,d ) + (1 − κ) log2 (1 + γ1,d + γ2,d ) < R)   = Pr (1 + γ1,d )κ (1 + γ1,d + γ2,d )(1−κ) < 2R   2R −1     R 1 x b(x) 2 −1 − dx, exp − − = 1 − exp − γ 1,d γ 1,d γ 1,d γ 2,d 0 78 9 6 Φ(γ 1,d ,γ 2,d ,R,κ)

3.3 Coded Cooperation

111

where the integral above is defined as Φ(γ 1,d , γ 2,d , R, κ) and b(x) 

2R/(1−κ) − 1 − x. (1 + x)κ/(1−κ)

The conditional outage probability of user 2 is obtained more easily as   R/κ 2 −1 (c) . pout,2 = Pr (κ log2 (1 + γ2,d ) < R) = 1 − exp − γ 2,d Case (d): Case (d) is exactly the same as Case (c) but with the roles of user 1 and user 2 reversed. The probability that is event occurs is given by   R/κ    R/κ + * −1 −1 2 2 c exp − . qd = Pr O1,2 ∩ O2,1 = 1 − exp − γ 1,2 γ 2,1 Similar to Case (c), the conditional outage probabilities of user 1 and user 2 are given respectively by   R/κ −1 2 (d) pout,1 = 1 − exp − γ 1,d 

and (d) pout,2



2R − 1 = 1 − exp − γ 2,d

 − Φ(γ 2,d , γ 1,d , R, κ).

By considering the above four cases, the average outage probability of user i, for i ∈ {1, 2}, can be obtained by (a)

(b)

(c)

(d)

pind out,i = qa · pout,i + qb · pout,i + qc · pout,i + qd · pout,i , where the superscript “ind” indicates that independent (or non-reciprocal) inter-user channels are considered. At high SNR, where SNR  0, it has been shown in [12] that the average outage probability can be approximated as     R/κ 2 − 1) (2 1 1 Λ(R, κ) ind pout,i ≈ +O , + 2 2 2 η2 ηi,d ηi,d ηj,d SNR2 SNR3 i,j where i = j ∈ {1, 2} and Λ(R, κ) was defined in (3.87). This shows that the user multiplexing scheme described above achieves diversity order of 2 for both users, even without the help of feedback from the relay. It is worthwhile to note that, when reciprocity holds on the inter-user channel (i.e., when h1,2 = h2,1 ), only Cases (a) and (b) will occur and, thus, the outage probability reduces to that derived in (3.86). At high SNR, the outage probability of user i can be approximated as

112

3 Two-User Cooperative Transmission Schemes

Fig. 3.19 10% outage capacity versus mean uplink SNR for various inter-user channel variances. Both the reciprocal and independent inter-user channels are considered (From c Hunter, Sanayei, and Nosratinia with modified labels. [2006] IEEE ).

prec out,i

1 ≈ SNR2



   (2R − 1)(2R/κ − 1) Λ(R, κ) 1 +O , + 2 2 2 η2 ηi,d ηi,d ηj,d SNR3 i,j

(3.89)

where i = j ∈ {1, 2}. Hence, diversity order of 2 is also achieved in this case. Moreover, one can observe that the outage probability in this case is lower than that achieved with independent inter-user channels. In Fig. 3.19 (i.e. [12, Figure 5]), the 10% outage capacities achieved by coded cooperation under independent and reciprocal inter-user channels are compared. The case with no user cooperation is also shown as a basis for comparison. The mean uplink SNRs are assumed to be the same for both users, i.e., γ 1,d = γ 2,d , and the statistics of the inter-user channels in both 2 2 directions are also assumed to be identical, i.e., η1,2 = η2,1 . The outage capacities are given for 3 different scenarios: the scenario with noiseless inter-user 2 2 channels (i.e., η1,2 = η2,1 = ∞), the scenario where the inter-user and up2 2 2 2 link channels have the same variance (i.e., η1,d = η2,d = η1,2 = η2,1 ), and the scenario where the inter-user SNR is 10dB less than the uplink SNR. We can observe that coded cooperation performs better over reciprocal interuser channels. The reason is that, with independent inter-user channels, the second portion of a certain user’s codeword will not be transmitted by any

3.3 Coded Cooperation

113

user in either Case (c) or Case (d), thereby increasing the outage probability. Such negligence of a certain user’s parity symbols in Phase II does not occur with reciprocal inter-user channels since the inter-user transmissions are either both successful or both unsuccessful. In Fig. 3.19, we can see that coded cooperation provides 4 − 7 dB performance gain compared to direct transmission for outage capacity R = 1. In the CC scheme with independent inter-user channels, the conditional outage probabilities increase considerably for Cases (c) and (d) due to negligence of a certain user’s parity symbols in Phase II of these cases. To avoid this loss in performance, one can employ a strategy that allows each user to use a portion of its transmission power to forward its own codeword, even when it successfully decodes its partner’s message. This scheme is referred to as the space-time coded cooperation (STCC) scheme [12, 14]. More specifically, upon successfully decoding its partner’s message, a user will only utilize power (1 − β)P to forward the second portion of its partner’s codeword while utilizing the remaining βP to transmit its own codeword. The codewords of different users are transmitted on their respective channels. Similarly, four possible cases may occur throughout the transmission process: Case (a) where both users successfully decode their partner’s messages, Case (b) where both user fail to decode their partner’s messages, Case (c) where only user 2 decodes its partner’s message, and Case (d) where only user 1 successfully decodes. In contrast to the previous scheme, no codeword is completely neglected in Cases (c) and (d) since each user will expend a portion of its power to forward its own message, even when it successfully decodes its partner’s message. Space-time coded cooperation was first proposed in [14] to enable the use of coded cooperation over fast fading channels. In fast fading environments, the channels in the both phases of the transmission process are assumed to be independent and, thus, utilizing the help from its partner may not always be advantageous. Reserving part of the resource to transmit its own parity symbols can thus reduce the probability of outage. In Fig. 3.20 (i.e., [12, Figure 4]), the outage probability of the space-time coded cooperation scheme is plotted for the case with reciprocal inter-user channels and the case with independent inter-user channels. The rate is set as R = 1/2 b/s/Hz. The mean uplink SNR for both user are assumed to be equal and the variances of the inter-user channels in both directions are also assumed to be equal. The channel parameters are given the same as those used in Fig. 3.19. One can observe that the original CC scheme achieves a lower average outage probability compared to STCC. However, when Cases (c) or (d) occur, STCC is able to avoid the significant increase in outage probability caused by the negligence of a certain user’s codeword.

114

3 Two-User Cooperative Transmission Schemes

Fig. 3.20 Performance comparison of coded cooperation and space-time coded cooperation over various slow fading channels (From Hunter, Sanayei, and Nosratinia with modc ified labels. [2006] IEEE ).

3.4 Compress-and-Forward Relaying Schemes Compress-and-forward (CF) relaying schemes refer to cases where the relay forwards quantized, estimated, or compressed versions of its observation to the destination. In contrast to DF or CC schemes, the relay in CF schemes need not decode perfectly the source message but need only to extract, from its observation, the information that is most relevant to the decoding at the destination. The amount of information extracted and forwarded to the destination depends on the capacity of the r-d link. In fact, it has been shown in [17] that CF can outperform DF when the relay is farther from the source (i.e., decoding is less reliable at the relay) and is closer to the destination (i.e., more information can be conveyed to the destination through the r-d channel). Moreover, CF also provides a more general form of compression compared to the simple scaling done in AF. The works on CF stem from the fundamental studies on relay channels [3, 17] and have mostly been investigated from the information-theoretic perspective. Detailed discussions of these schemes will be delayed till Section 5.1.3, but the basic concepts are introduced in the following.

3.5 Channel Estimation in Single Relay Systems

115

Fig. 3.21 System model of compress-and-forward relaying scheme.

The CF relaying scheme also takes on two phases of transmission, as illustrated in Fig. 3.21. In Phase I, the source transmits a message to both the relay and the destination, where the received signals are denoted by Yr and (1) Yd , respectively; in Phase II, the relay compresses Yr or extracts from Yr the information that is most useful for the decoding at the destination. The compression at the relay must be performed with only statistical knowledge (1) of the signal received at the destination, i.e., Yd , which relates to the works on distributed source coding [26] or source coding with side information [29]. One approach to achieve this task is the use of Wyner-Ziv coding (WZC) at the relay. The basic framework pioneered by Wyner and Ziv in [29] is illustrated in Fig. 3.22. Here, the lossy source coding problem is considered where the source W is compressed under a given distortion constraint. It has been shown in [29] that, with knowledge of the joint distribution of the input W and side information S (which is only available at the decoder), the input W can be compressed with a rate that is smaller than that attainable without side information. WZC has been applied to many different applications, e.g., distributed video coding, and can implemented using practical coding schemes. When applying WZC to the relay problem shown in Fig. 3.21, the relay observation Yr can now be viewed as the input W and the signal re(1) ceived at the destination, i.e., Yd , can be viewed as the side information S that is available at the decoder. The capacity of the r-d link determines the maximum compression rate at the relay and, thus, affects the distortion of the reconstructed signal Yˆr . More practical studies on CF relaying schemes can be found in [9, 15, 20, 21].

3.5 Channel Estimation in Single Relay Systems In previous sections, DF, AF, CC, and CF relaying schemes have been introduced by assuming different levels of transmitter CSI and by assuming perfect CSI at the receiver for coherent detection. In practice, the CSI required

116

3 Two-User Cooperative Transmission Schemes

Fig. 3.22 Block diagram of Wyner-Ziv coding.

to achieve these tasks must be obtained through actual channel estimation. Without an accurate estimate of the channels, the benefits of cooperation may decrease substantially. However, the problem is less complex in the case of DF, CC, and CF schemes, where decoding is performed at both the relay and the destination. In these cases, the channel coefficients of each point-to-point link (i.e., s-r, r-d, and s-d links) can be estimated separately at the relay and the destination using conventional channel estimation schemes [16]. However, in AF relaying schemes, the effective channel received at the destination is a combination of the s-r and the r-d channels and, thus, the estimation performances may differ from that in point-to-point channels. In this section, we briefly touch upon these issues by summarizing the results presented in [23]. Let us consider the basic AF relaying model presented in Section 3.2 that consists of a source, a relay, and a destination. In each time slot, the source first transmits a symbol to the relay in the first half of the time slot while the relay amplifies and forwards the symbol to the destination in the second half of the time slot. Suppose that the symbol xs [m]  is transmitted by the source in the m-th symbol period and that E |xs [m]|2 = Ps , where the source power has been incorporated into the symbol xs [m] to simplify notations. The signal received at the relay during the m-th symbol period can be expressed as yr [m] = hs,r [m]xs [m] + wr [m] 2 2 where hs,r [m] ∼ CN (0, ηs,r ) and wr [m] ∼ CN (0, σw ). Different from previous sections, we assume here the more general case where the channel coefficients, e.g., hs,r [m] may vary from symbol to symbol based on a certain correlation model. The relay then transmits the signal G[m]yr [m] to the destination, where the received signal is given by

yd [m] = hr,d [m]G[m]yr [m] + wd [m] = G[m]hr,d [m]hs,r [m]xs [m] + G[m]hr,d [m]wr [m] + wd [m], 2 2 where hr,d [m] ∼ CN (0, ηr,d ) and wd [m] ∼ CN (0, σw ). The value of the amplifying gain depends on the specific AF scheme that is employed. For variable gain relaying, we have

3.5 Channel Estimation in Single Relay Systems

117

Fig. 3.23 An example of pilot-selection for channel estimation with Np = 4.

' G[m] =

' Pr = E [|yr [m]|2 |hs,r [m]]

Pr , 2 Ps |hs,r [m]|2 + σw

(3.90)

and, for fixed gain relaying, we have ' ' Pr Pr =  G, G[m] = 2 2 2 E [|yr [m]| ] Ps ηs,r + σw

(3.91)

which is constant over all symbol periods. Notice that the relay power Pr is also incorporated into the channel gains here. In order to perform coherent detection on the symbol transmitted through the s-r-d path, the destination must have knowledge of the effective channel coefficient heff [m] = G[m]hr,d [m]hs,r [m]. Even though the s-d channel may also be of interest when considering diversity combining, it can be easily obtained using conventional point-to-point channel estimation schemes and, thus, will not be considered in the following. Specifically, we shall focus on the estimation of heff [m], which is no longer Gaussian due to the multiplication of channel coefficients and which may be affected by the additional noise propagation term. √ (p) Suppose that a binary pilot symbol, say xs [ ] ∈ {± Ps }, is inserted every Tp symbol periods, where is a position of the pilot and the superscript (p) represents the fact that it is a pilot symbol. To obtain an accurate estimate of the channel, the destination takes the Np nearest pilot signals (with respect to the symbol of interest) to perform channel estimation. For example, if Np = 4, the destination will select the two pilot signals directly before and after the symbol of interest, as shown in Fig. 3.23. The signal received during each pilot symbol period is first normalized by its corresponding training symbol to ( )T ˆ (p) [ ] = y (p) [ ]/x(p) ˆ (p) = h ˆ (p) [ 1 ], h ˆ (p) [ 2 ], . . . , h ˆ (p) [ Np ] obtain h s [ ]. Let h eff

d

eff

eff

eff

eff

be the vector of Np normalized pilot signals used for channel estimation. In

118

3 Two-User Cooperative Transmission Schemes

conventional point-to-point channels, one of the most widely adopted channel estimation schemes is the minimum mean square error (MMSE) estimator. However, this estimator is difficult to obtain for the AF relay channel due to the non-Gaussian nature of the effective channel coefficient and the noise propagation effect. Therefore, we instead employ the Linear MMSE (LMMSE) estimator which can also be effective as shown in the following. By employing the LMMSE estimator, the estimate of the channel at time m can be expressed as ˆ eff [m] = r h h

ˆ (p) eff heff

ˆ (p) [m] · R−1 ·h eff ˆ (p) ˆ (p) heff heff

(3.92)

) ) ( ( ˆ (p) )H and R ˆ p ˆ (p) = E h ˆ (p) (h ˆ (p) )H . where rh hˆ (p) [m] = E heff [m] · (h eff eff eff heff heff eff eff The cross correlation between the effective channel and a normalized pilot signal is ( )  * ˆ (p)∗ [ ] = E G[m]hr,d [m]hs,r [m] G[ ]h∗ [ ]h∗ [ ] E heff [m]h r,d s,r eff +) ∗ (p) +G[ ]h∗r,d [ ]wr∗ [ ]/x(p) [ ] + w [ ]/x [ ] s d s and the correlation between two normalized pilot signals is ( ) ˆ (p) [ ]h ˆ (p)∗ [  ] E h eff eff ( (p) = E (G[ ]hr,d [ ]hs,r [ ] + G[ ]hr,d [ ]wr [ ]/x(p) s [ ] + wd [ ]/xs [ ])

)  ∗  (p)  ×(G[  ]h∗r,d [  ]h∗s,r [  ] + G[  ]h∗r,d [  ]wr∗ [  ]/x(p) s [ ] + wd [ ]/xs [ ]) .

For fixed gain relaying, the amplifying gain G[m] is constant regardless of the time index m. In this case, we have ( ) ˆ (p)∗ [ ] = G2 Rh h [m − ]Rh h [m − ] E heff [m]h s,r s,r r,d r,d eff =

Pr Rhs,r hs,r [m − ]Rhr,d hr,d [m − ], 2 (1 + γ σw s,r )

2 2 where γ s,r = Ps ηs,r /σw is the average SNR of the s-r link, and

3.5 Channel Estimation in Single Relay Systems

119

( ) ˆ (p) [ ]h ˆ (p)∗ [  ] E h eff eff = G2 Rhs,r hs,r [ −  ]Rhr,d hr,d [ −  ] +

2 2 σw G2 ηr,d

Ps Pr  Rh h [ − ]Rhr,d hr,d [ −  ] = 2 σw (1 + γ s,r ) s,r s,r +

δ( −  ) +

2 σw δ( −  ) Ps

2 γ r,d σw σ2 δ( −  ) + w δ( −  ), (1 + γ s,r )Ps Ps

2 2 where γ r,d = Pr ηr,d /σw is the average SNR of the r-d link. For variable gain relaying, the amplifying gain then depends on the source-relay channel during each symbol period and, thus, must be estimated at the relay as well. We may also take the LMMSE estimate of this channel as

ˆ s,r [m] = r ˆ (p) [m] · R−1 ˆ (p) h ·h s,r ˆ (p) ˆ (p) hs,r h hs,r hs,r

s,r

(3.93)

( )T (p) (p) (p) ˆ (p) ˆ (p) ˆ (p) ˆ (p) where h and hs,r [ ] = yr [ ]/xs [ ]. s,r = hs,r [ 1 ], hs,r [ 2 ], . . . , hs,r [ Np ] By substituting this into the amplifying gain G[m], the correlation between the effective channel coefficients can be approximated as [24]   ) P ( e1/γ s,r E1 (1/γ s,r ) r (p)∗ ˆ 1− Rhs,r hs,r [m − ]Rhr,d hr,d [m − ] E heff [m]h eff [ ] ≈ Ps γ s,r and   ( ) P e1/γ s,r E1 (1/γ s,r ) r (p) ˆ (p)∗  ˆ 1− Rhs,r hs,r [ −  ]Rhr,d hr,d [ −  ] E heff [ ]heff [ ] ≈ Ps γ s,r +

2 γ r,d σw σ2 e1/γ s,r E1 (1/γ s,r )δ( −  ) + w δ( −  ), Ps γ s,r Ps



where E1 (z) =

z

∞

e−t dt t

is the exponential integral function [5]. Example: Downlink Relay System Let us consider a downlink system where the base station (BS) transmits a message to a mobile station (MS) through a relay station. Depending on the mobility of the relay, two scenarios can be considered, namely, the case with stationary relays and the case with mobile relays. Following the model given in [13] and [23], the temporal correlations of the channels in the case of stationary relays are given by

120

3 Two-User Cooperative Transmission Schemes

 2 Rhs,r hs,r [k] = E hs,r [ + k]h∗s,r [ ] = ηs,r J0 (2πfs,r k)

(3.94a)

 2 Rhr,d hr,d [k] = E hr,d [ + k]h∗r,d [ ] = ηr,d J0 (2πfr,d k),

(3.94b)

and

where J0 (x) is the zeroth order Bessel function of the first kind and fs,r , fr,d are the Doppler frequencies associated with the channels hs,r [ ] and hr,d [ ], respectively. In the case of mobile relays, the channel correlations are given by  2 Rhs,r hs,r [k] = E hs,r [ + k]h∗s,r [ ] = ηs,r J0 (2πfs,r k) (3.95a) and  2 J0 (2πfs,r k)J0 (2πfr,d k). Rhr,d hr,d [k] = E hr,d [ + k]h∗r,d [ ] = ηr,d

(3.95b)

To obtain reliable estimates of the channels, the pilots must be placed accord1 ing to the Nyquist sampling criterion so that Tp ≤ 2fmax Ts , where fmax is the maximum Doppler frequency of the estimated channel and Ts is the symbol duration. Notice that fmax of the effective channel is fs,r + fr,d for stationary relays and is 2fs,r + fr,d for mobile relays. It is interesting to note that, in the DF scheme, the source-to-relay and the relay-to-destination channels are estimated separately and pilots need only be placed at twice the maximum Doppler frequency of the two links, which is max{fs,r , fr,d } for stationary relays and fs,r + fr,d for mobile relays. That is, the pilots required in the AF scheme is generally much larger than that of the DF scheme. However, the DF scheme require more complex operations at the relay. By assuming Np = 4, Tp = 5, and equal average SNR on each hop (i.e., γ s,r = γ r,d ), the BER performance versus the SNR per hop are shown in Fig. 3.24 and Fig. 3.25 (see [23]). We can see that the performance loss due to channel estimation errors in the AF case is comparable with that in the DF case. This shows that it is indeed sufficient to estimate the combined channel (instead of to estimate each channel separately) in the AF case. Moreover, the figures show no loss of diversity gain due to channel estimation errors.

References 1. Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, New York (1965) 2. Chen, D., Laneman, J.N.: Modulation and demodulation for cooperative diversity in wireless systems. IEEE Transactions on Wireless Communications 5(7), 1785–1794 (2006) 3. Cover, T., El Gamal, A.: Capacity theorems for the relay channel. IEEE Transactions on Information Theory 25(5), 572–584 (1979) 4. Gradshteyn, I.S., Ryzhik, I.M., Jeffrey, A. (eds.): Table of Integrals, Series, and Products, 5 edn. Academic Press (1994)

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Fig. 3.24 For Np = 4 and Tp = 5, the BER vs SNR per hop (i.e., γ s,r = γ r,d ) is shown for the case with stationary relays (i.e. fs,r Ts = 0.001 and fr,d Ts = 0.01). (From Patel c and St¨ uber. [2007] IEEE ).

Fig. 3.25 For Np = 4 and Tp = 5, the BER vs SNR per hop (i.e., γ s,r = γ r,d ) is shown uber. for the case with mobile relays (i.e. fs,r Ts = fr,d Ts = 0.01). (From Patel and St¨ c [2007] IEEE ).

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3 Two-User Cooperative Transmission Schemes

5. Gradshteyn, I.S., Ryzhik, I.M., Jeffrey, A., Zwillinger, D. (eds.): Table of Integrals, Series, and Products, 7 edn. Academic Press (2007) 6. Hasna, M.O., Alouini, M.-S.: End-to-end performance of transmission systems with relays over Rayleigh-fading channels. IEEE Transactions on Wireless Communications 2(6), 1126–1131 (2003) 7. Hasna, M.O., Alouini, M.-S.: A performance study of dual-hop transmissions with fixed gain relays. IEEE Transactions on Wireless Communications 3(6), 1963 (2004) 8. Hong, Y.-W., Huang, W.-J., Chiu, F.-H., Kuo, C.-C.J.: Cooperative communications in resource-constrained wireless networks. IEEE Signal Processing Magazine 24(3), 47–57 (2007) 9. Hu, R., Li, J.: Practical compress-and-forward in user cooperation: Wyner-Ziv cooperation. In: Proceedings on the IEEE International Symposium on Information Theory (ISIT) pp. 489-493 (2006) 10. Hunter, T., Nosratinia, A.: Cooperative diversity through coding. In: Proceedings on the IEEE International Symposium on Information Theory (ISIT) pp. 220 (2002) 11. Hunter, T.E., Nosratinia, A.: Diversity through coded cooperation. IEEE Transactions on Wireless Communications 5(2), 283–289 (2006) 12. Hunter, T.E., Sanayei, S., Nosratinia, A.: Outage analysis of coded cooperation. IEEE Transactions on Information Theory 52(2), 375–391 (2006) 13. Jakes, W.C.: Microwave Mobile Communications, 2 edn. IEEE Press (1994) 14. Janani, M., Hedayat, A., Hunter, T.E., Nosratinia, A.: Coded cooperation in wireless communications: Space-time transmission and iterative decoding. IEEE Transactions on Signal Processing 52(2), 362–371 (2004) 15. Jiang, J., Thompson, J.S., Grant, P.M., Goertz, N.: Practical compress-and-forward cooperation for the classical relay network. In: Proceedings of the 17th European Signal Processing Conference (EUSIPCO), pp. 2421–2425. Glasgow, Scotland (2009) 16. Kay, S.M.: Fundamentals of Statistical Signal Processing: Estimation Theory, vol. I. Prentice Hall PTR (1993) 17. Kramer, G., Gastpar, M., Gupta, P.: Cooperative strategies and capacity theorems for relay networks. IEEE Transactions on Information Theory 51(9), 3037–3063 (2005) 18. Laneman, J.N., Tse, D.N.C., Wornell, G.W.: Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Transactions on Information Theory 50(12), 3062–3080 (2004) 19. Laneman, J.N., Wornell, G.W.: Energy-efficient antenna sharing and relaying for wireless networks. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), pp. 7-12 (2000) 20. Liu, Z., Uppal, M., Stankovic, V., Xiong, Z.: Compress-forward coding with BPSK modulation for the half-duplex gaussian relay channel. In: Proceedings of the IEEE ISIT, pp.2395 -2399 (2008) 21. Liu, Z., Uppal, M., Stankovic, V., Xiong, Z.: Compress-forward coding with BPSK modulation for the half-duplex gaussian relay channel. IEEE Transactions on Signal Processing 57(11), 4467–4481 (2009) 22. Nosratinia, A., Hunter, T.E., Hedayat, A.: Cooperative communication in wireless networks. IEEE Communications Magazine 42(10), 74–80 (2004) 23. Patel, C.S., St¨ uber, G.L.: Channel estimation for amplify and forward relay based cooperation diversity systems. IEEE Transactions on Wireless Communications 6(6), 2348–2356 (2007) 24. Patel, C.S., St¨ uber, G.L., Pratt, T.G.: Statistical properties of amplify and forward relay fading channels. IEEE Transactions on Vehicular Technology 55(1), 1–9 (2006) 25. Sendonaris, A., Erkip, E., Aazhang, B.: User cooperation diversity–Part I: System description” and “User cooperation diversity–Part II: implementation aspects and performance analysis. IEEE Transactions on Communications 51(11), 1927-1938 and 1939-1948 (2003) 26. Slepian, D., Wolf, J.: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 19(4), 471–480 (1973)

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27. Su, W., Sadek, A.K., Liu, K.J.R.: Cooperative communications protocols in wireless networks: Performance analysis and optimum power allocation. Wireless Personal Communications 44(2), 181–217 (2008) 28. Wang, T., Cano, A., Giannakis, G.B., Laneman, J.N.: High-performance cooperative demodulation with decode-and-forward relays. IEEE Transactions on Communications 55(7), 1427–1438 (2007) 29. Wyner, A., Ziv, J.: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 22(1), 1–10 (1976) 30. Zhang, J., Zhang, Q., Shao, C., Wang, Y., Zhang, P., Zhang, Z.: Adaptive optimal transmit power allocation for two-hop non-regenerative wireless relay system. In: Proceedings of IEEE 59th Vehicular Technology Conference, vol. 2, pp. 1213- 1217(2004) 31. Zhang, Q., Zhang, J., Shao, C., Wang, Y., Zhang, P., Hu, R.: Power allocation for regenerative relay channel with Rayleigh fading. In: Proceedings of IEEE 59th Vehicular Technology Conference, vol. 2, pp. 1167– 1171 (2004)

Chapter 4

Cooperative Transmission Schemes with Multiple Relays

In this chapter, cooperative transmission schemes are introduced for networks that consist of more than two users. Following the assumptions made in the previous chapter, we assume that, at each instant in time, only one user acts as the source while the other users serve as relays that help forward the source’s message to the destination. In this case, the relays can together form a distributed antenna array and adopt conventional MIMO signal processing techniques, such as beamforming, antenna selection, or space-time coding etc., to enhance communication performance. As the number of relays increases, more radio resources and more degrees of freedom can be pooled together and utilized jointly to assist the source’s transmission. However, to exploit these advantages, one must overcome the challenges posed by the individual resource constraints and the lack of coordination among relays. In Section 4.1, we first introduce cooperation schemes where relays are assumed to transmit over orthogonal channels. These orthogonal cooperation schemes can be implemented without strict synchronization requirements but result in low bandwidth efficiency. Then, in Sections 4.2–4.4, we introduce non-orthogonal cooperation schemes where all relays are assumed to share a common channel. Under different CSI assumptions, we introduce three cooperative transmission schemes, namely, distributed beamforming (BF), selective relaying (SR), and distributed space-time coding (DSTC). Each of these schemes can be incorporated with both AF and DF relays. The CSI can be obtained through channel estimation at the destination, which will be discussed in Section 4.5. More advanced multi-relay cooperation strategies such as multi-hop and asynchronous transmission schemes will also be discussed in Section 4.6.

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_4, © Springer Science+Business Media, LLC 2010

125

126

4 Cooperative Transmission Schemes with Multiple Relays

Fig. 4.1 System model of a two-hop synchronous cooperation system.

4.1 Orthogonal Cooperation Consider a cooperative network that consists of L + 1 users, one acting as the source and L serving as the relays, as shown in Fig. 4.1. Let us denote the source by s, the destination by d, and label the relays from 1 to L. In orthogonal cooperation schemes, we assume that the source and the relays each transmit over an orthogonal time or frequency channel. Similar to the two-user scenario described in the previous chapter, all nodes are assumed to be half-duplex so that cooperative transmission must be conducted over two phases. In Phase I, the source first transmits the symbol vector xs = [xs [0], . . . , xs [M − 1]]T (with E[|xs [m]|2 ] = 1, ∀m) to both the relays and the destination. The signals received at relay and the destination in the m-th symbol period are given by y [m] = hs, Ps xs [m] + w [m], (4.1) (1) (1) yd [m] = hs,d Ps xs [m] + wd [m], (4.2) where Ps is the source transmission power, hs, and hs,d are the channel coefficients between source and relay (i.e., the s- link) and between source and destination (i.e., the s-d link), respectively, and w [m] ∼ CN (0, σ2 ) and (1) wd [m] ∼ CN (0, σd2 ) are the additive white Gaussian noise (AWGN) at relay

and the destination, respectively. In Phase II, the relays belonging to a certain cooperative set D will each forward the source’s message over an orthogonal channel. That is, relay

∈ D will transmit, in its corresponding channel, the symbol vector x = [x [0], . . . , x [M − 1]]T = f (y ) to the destination, where E[|x [m]|2 ] = 1, ∀m, and f (·) is the relay function that depends on the specific cooperative strategy. The signal received by the destination in the channel corresponding to relay is given by

4.1 Orthogonal Cooperation (2,)

yd

127

[m] = h,d

(2,) P x [m] + wd [m],

(4.3)

where P is the transmission power of relay , h,d is the channel coeffi(2,) cient between relay and the destination (i.e., the -d link), and wd [m] ∼ 2 CN (0, σd ) is the AWGN at the destination in the channel corresponding to relay . All channel coefficients are assumed to be independent of each other. The superscript (2, ) is used to highlight the fact that the signals from different relays are received over different time or frequency channels. In the following, we discuss both AF- and DF-based orthogonal cooperation schemes and analyze their performance under repetition-based coding schemes. These schemes can be viewed as direct extensions of the basic AF and DF schemes described in Chapter 3.

4.1.1 Orthogonal Cooperation with AF Relays In the AF orthogonal cooperation scheme, we assume that all relays participate in the cooperative transmission regardless of the quality of the received signal. Therefore, the cooperative set is given by D = {1, 2, · · · , L}. The signal forwarded by relay during Phase II is given by y [m] y [m] x [m] = , =2 E[|y | [m]] Ps |hs, |2 + σ2

(4.4)

for m = 0, . . . , M − 1. Suppose that the source and the relays are each allocated an equal-length time slot for the transmission of their codewords. Thus, a total of L + 1 orthogonal time slots are required throughout the cooperative transmission. The signals received at the destination in Phase I and in the -th time slot of Phase II are given by (1) (1) yd [m] = Ps hs,d xs [m] + wd [m], and (2,)

yd

[m] = ' =

-

(2,)

P h,d x [m] + wd

[m]

P Ps h,d hs, xs [m]+ Ps |hs, |2 +σ2

' P (2,) h,d w [m]+wd [m], Ps |hs, |2 +σ2

respectively. By employing diversity combing at the destination, the signals received over the L + 1 time slots can be combined using the maximal-ratio combiner (MRC), which yields the output signal

128

4 Cooperative Transmission Schemes with Multiple Relays

√ L  Ps h∗s,d (1) y:d [m] = y [m] + d σd2



P Ps h∗ h∗ Ps |hs, |2 +σ2 ,d s,

P |h |2 σ2 =1 Ps |hs, |2 +σ2 ,d

+ σd2

(2,)

yd

[m].

(4.5)

The effective SNR at the output of the MRC is given by γeff = γs,d +

L  =1

γs, γ,d , γs, + γ,d + 1

(4.6)

where γs,d = Ps |hs,d |2 /σd2 , γs, = Ps |hs, |2 /σ2 , and γ,d = P |h,d |2 /σd2 . With a given instantaneous CSI, the rate achievable with the AF orthogonal cooperation scheme can be expressed as   L  γs, γ,d 1 CAF (γ) = log2 1 + γs,d + , (4.7) L+1 γs, + γ,d + 1 =1

where γ = [γs,d , γs,1 , . . . , γs,L , γ1,d , . . . , γL,d ]. The unit is given by bits per channel use (bpcu). Given the average transmission rate R, the outage probability can be computed as pout = Pr (CAF (γ) < R)     L  γs, γ,d 1 log2 1 + γs,d + 0, then λ + μ will +2  2* 1 and, thus, (c7) will not hold. be strictly greater than 1/ σw + σε2 η2 w

,d

Similarly, we can also multiply both sides of (c7) by E − ε to obtain ⎛ ⎞ 1 (E − ε ) ⎝− * (4.143) +2 + λ − ν ⎠ = 0. 1 2 σw η2 + σε2 w

,d

+2  2* 1 , then it must also be true that λ − ν ≤ + σE2 Similarly, if λ ≤ 1/ σw 2 η,d w +  2* 1 E 2 . In this case, ε must be equal to its maximum value E . 1/ σw η2 + σ2 w ,d +2  2* 1 + σε2 Otherwise, if ε < E , then λ−ν must be strictly less than 1/ σw η2 ,d

and, thus, again (c7) will not hold.  2* 1 Now, we can also show that, for 1/ σw + η2 ,d

+ E 2 2 σw

w

4 2 < λ < η,d /σw , both

μ and ν must be 0. From (4.142), we know that ⎞ ⎛ 1 ε ⎝− * + + λ⎠ ≤ 0. ε 2 1 2 σw + σ2 η2 w

,d

 2* 1 Therefore, if λ > 1/ σw + η2 ,d

+ E 2 , then ε < E must hold and, thus, σ2 w

μ = 0 by (c5). Moreover, by (4.143), we also know that ⎛ ⎞ 1 (E − ε ) ⎝− * + + λ⎠ ≥ 0. ε 2 1 2 σw + 2 2 σ η ,d

w

4 2 Therefore, if λ < η,d /σw , then ε > 0 and, thus, ν = 0 by (c6). Hence, it follows that  2 1 σw σ2 − * − 2w . + λ = 0 ⇒ ε = (4.144) + 2 λ η,d σ 2 21 + ε2 w η ,d

σw

In summary, the optimal power allocation is given by [9]

4.5 Channel Estimation in Multi-Relay Systems

177

Fig. 4.13 Illustration to the cave-filling strategy for (4.145).

⎧ ⎪ 0, ⎪ ⎪  ⎪ ⎪ 2 ⎨ σw λ − ε = ⎪ ⎪ ⎪ ⎪ E ⎪ ⎩

for λ ≥ 2 σw 2 , η,d

for

2 σw

*

for λ ≤

4 η,d 2 , σw

1 1 η2 ,d 2 σw

E + σ2 w

*

+2 < λ
γth , γ1,2 > γth , · · · , γL,L+1 > γth ) = 1−

L 

Pr (γ,+1 > γth ) ,

=0

where the SNRs γ,+1 , for = 0, . . . , L, are assumed to be independent. Under the Rayleigh fading assumption, the probability inside the product term can be approximated at high SNR as

180

4 Cooperative Transmission Schemes with Multiple Relays

Fig. 4.16 Illustration of a multi-hop cooperative network with diversity combining only at the destination.

  γth γth ≈1− Pr (γ,+1 > γth ) = exp − γ ,+1 γ ,+1 and, thus, pout = 1 −

L+1  =0

   L γth γth exp − ≈ . γ ,+1 γ ,+1 =0

This shows that the outage probability increases as the number of hops increases. However, in practice, relays in multi-hop networks will have the ability to buffer the message and retransmit it in later time slots if previous transmission attempts have failed. Hence, as long as the end-to-end delay is tolerable, the message will eventually reach the destination with high probability. In the following, let us consider a slightly more advanced scenario where diversity combining is performed at the destination, but the intermediate relays only receive from its immediate upstream node, as illustrated in Fig. 4.16. By assuming that all intermediate relays are able to successfully decode the source’s message, the outage probability at the destination L + 1 can be computed as   L  pout,L+1 = Pr (4.146) γk,L+1 < γth . k=0

To obtain a closed-form expression of this probability, let us consider the worst case scenario where the distribution of the SNRs on each link is equal to that of the worst link, e.g., the distribution of γ0,L+1 . In this case, the L summation γsum  k=0 γk,L+1 becomes a chi-squared random variable with  2(L + 1) degrees of freedom and mean equal to L k=0 γ k,L+1 = (L + 1)γ 0,L+1 . Recall that a chi-squared random variable Y with 2ν degrees of freedom and mean 2νσ 2 has CDF given by

4.6 Other Topics on Multi-Relay Cooperative Communications

181

 x  ν−1  1  x k FY (y) = Pr(Y ≤ y) = 1 − exp − 2 . 2σ k! 2σ 2

(4.147)

k=0

In the worst case, the outage probability in (4.146) equals to pout,L+1

  k  L 1 γth γth = 1 − exp − . γ 0,L+1 k! γ 0,L+1 k=0

By Taylor expansion, we have  L  ∞  1  −γth k  1  γth k . (4.148) pout,L+1 = 1 − k! γ 0,L+1 k! γ 0,L+1 k=0

k=0

It is worthwhile to note that, for 0 ≤ ≤ L, the -th power of γth /γ 0,L+1 is canceled out because −

  k=0

(−1)k k!( − k)!



γth



γ 0,L+1

 = −(1 − 1)

γth γ 0,L+1

 = 0.

On the other hand, the (L + 1)-th power of γth /γ 0,L+1 equals to −

L+1  k=1

(−1)k k!(L + 1 − k)!



γth

L+1

γ 0,L+1

 L+1 L+1  (−1)k γth 1 − = (L + 1)! k!(L + 1 − k)! γ 0,L+1 k=0  L+1 1 γth = . (L + 1)! γ 0,L+1 

Therefore, at high SNR, the outage probability can be approximated by   L+1 L+2  γth 1 γth pout,L+1 = +O (L + 1)! γ 0,L+1 γ 0,L+1  L+1 γth 1 . (4.149) ≈ (L + 1)! γ 0,L+1 Similarly, with different expected SNR on each link, the outage probability in (4.146) can be approximated at high SNR by [29] pout,L+1

L L+1  γth 1 ≈ . (L + 1)! γ k,L+1 k=0

(4.150)

182

4 Cooperative Transmission Schemes with Multiple Relays

Fig. 4.17 Illustration of a multi-hop cooperative network with diversity combining at all nodes.

Since this probability is conditioned on the fact that all intermediate relays successfully decode the message, the end-to-end outage probability must then take into consideration the outage probability at each relay , which is given by pout, = Pr (γ−1, < γth ) = 1 − exp(−

γth ), γ −1,

(4.151)

for = 1, . . . , L. The end-to-end outage probability is then given by pout = 1 −

L+1 

(1 − pout, ).

(4.152)

=1

Notice that, if each intermediate relay can also combine signals transmitted from all of its upstream nodes, the end-to-end outage probability can be further improved. In this case, the outage probability at each intermediate relay can be computed similar to that in (4.146) and (4.150). That is, by assuming that all upstream nodes have successfully decoded the message, the outage probability at node can be approximated at high SNR by   −1  −1   1 γth pout, = Pr γk, < γth ≈ , (4.153)

! γ k, k=0

k=0

for = 1, . . . , L + 1. By substituting this into (4.152), we can then obtain the expression for the end-to-end outage probability with diversity combining at all nodes.

Multihop Transmissions with AF Relays Although most conventional multi-hop systems adopt DF relaying techniques, the multi-hop transmission can be achieved similarly by using AF relaying

4.6 Other Topics on Multi-Relay Cooperative Communications

183

techniques. Specifically, consider an AF multi-hop network, similar to that shown in Fig. 4.15, where each intermediate node amplifies and forwards its received signal to its downstream nodes. We first examine the case without diversity combining, where each node receives only from its immediate upstream node. Following the signal model given in Section 3.2, the signal received at node can be expressed as y = h−1, P−1 x−1 + w , (4.154) where x−1 = β−1 y−1

(4.155)

is the signal transmitted by node − 1 and w is the noise at node . The amplifying gain imposed by relay − 1 is given by β−1 =

1 1 . = 2 E[|y−1 |2 ] P−2 |h−2,−1 |2 + σw

By substituting the expressions of {yk }−1 k=1 in (4.154) and (4.155), we can obtain an equivalent expression for y as y = : h0, P0 xs + w : , where : h0, = h0,1

√ −1  hk,k+1 Pk βk hk,k+1 Pk = h0,1 2 Pk−1 |hk−1,k |2 + σw k=1 k=1 −1 

is the effective multi-hop channel between 0 and , and  −1  −1   hk,k+1 βk Pk wm w : = w + m=1

= w +

−1  m=1

k=m

 −1 

 √ hk,k+1 Pk wm 2 Pk−1 |hk−1,k |2 + σw k=m

is the effective noise at node . Therefore, the effective signal power at the destination can be computed as |: h0,L+1 |2 P0 = |h0,1 |2 P0

L  k=1

= |h0,1 |2 P0

L  k=1

|hk,k+1 |2 Pk 2 Pk−1 |hk−1,k |2 + σw γk,k+1 , γk−1,k + 1

(4.156)

184

4 Cooperative Transmission Schemes with Multiple Relays

2 where γk,k+1 = |hk,k+1 |2 Pk /σw is the SNR of the link between nodes k and k + 1. Moreover, the effective noise variance at the destination can be computed as   L  L 2   P |h | k k,k+1 2 2 σw L+1 = σw 1 + 2 P |h |2 + σw m=1 k=m k−1 k−1,k    L L   γk,k+1 2 . (4.157) 1+ = σw γ +1 k−1,k m=1 k=m

By (4.156) and (4.157), the effective SNR of the multi-hop channel between source and destination is given by γ0,L+1

> γk,k+1 γ0,1 L h0,L+1 |2 P0 |: k=1 γk−1,k +1   = = >L  2 γk,k+1 σw L+1 1+ L m=1 k=m γk−1,k +1 >L k=0 γk,k+1 ( ) =  >L L+1 >m−1  −1,k + 1) (γ γ  k k,k+1 m=1 k =1 k=m  L+1  −1 m−1   1 1 = 1+ γ γk−1,k m=1 m−1,m k=1  L+1  −1   1 1+ −1 . = γk−1,k

(4.158)

(4.159)

(4.160)

(4.161)

k=1

To evaluate the outage probability of the AF multi-hop transmission scheme, we must first obtain the distribution of the effective SNR γ0,L+1 . Unfortunately, this is not easy to derive in closed form. However, at high SNR, we can approximate the effective SNR as γ0,L+1 ≈

L+1  k=1

1 γk−1,k

−1  γapprox .

(4.162)

Given the SNR threshold γth , the end-to-end outage probability is given by [10] + * −1 −1 pout ≈ Pr (γapprox < γth ) = Pr γapprox > γth   M1/γapprox (−s)  −1  , (4.163) = 1−L  s 1/γth

where L(·) is the inverse Laplace transform and M1/γapprox (s) = E[e−s/γapprox ] is the moment generating function (MGF) of 1/γapprox . By (4.162), the MGF of 1/γapprox is a product of the MGFs of 1/γ0,1 ,· · · , 1/γL,L+1. That is, for

4.6 Other Topics on Multi-Relay Cooperative Communications

185

$)6\VWHP ')6\VWHP

 Fig. 4.18 Outage probability versus number of hops for AF and DF multi-hop systems c without diversity combining. (From Hasna and Alouini with modified labels; [2003] IEEE.)

Rayleigh fading channels, we have M1/γapprox (s) =

L+1 

M1/γk−1,k (s),

(4.164)

k=1

'

where M1/γk−1,k (s) =

4s γ k−1,k

' K1

4s γ k−1,k

 (4.165)

the MGF of 1/γk−1,k and K1 (·) is the first order modified Bessel function of the second kind. For Nakagami fading channels with parameter mn , the MGF of 1/γk−1,k is given by     mn /2 mn s mn s 2 (4.166) K mn 2 M1/γk−1,k (s) = Γ(mn ) γ k−1,k γ k−1,k where Kmn (·) is the mn -th order modified Bessel function of the second kind. In Fig. 4.18, the outage probability is shown for both DF and AF multi-hop transmission schemes without diversity combining. In the simulations, each 2 hop is assumed to have equal SNR, i.e., γ k−1,k = Pk−1 ηk−1,k /σw = γ, ∀k,

186

4 Cooperative Transmission Schemes with Multiple Relays

and the ratio between the SNR on each hop and the SNR threshold is chosen such that γ/γth = 25 dB. We can observe that DF outperforms AF with an increasing gap between the two curves as the number of hops increases. In AF multi-hop relay systems, the signals transmitted by each node on the multi-hop route can also be combined at the destination to enhance the receive SNR. By considering a multi-hop network of L intermediate relays, the destination will receive a total of L + 1 copies of the message, including that transmitted by the source. The signal received at the destination from node can be written as () () () yL+1 = : h0,L+1 P0 xs + w :L+1 , where () : h0,L+1 = h0,1

 −1 

βk hk,k+1 Pk

 β h,L+1

P

k=1

√  h,L+1  hk,k+1 Pk = h0,1 2 h,+1 Pk−1 |hk−1,k |2 + σw k=1 is the effective channel between source and destination in the -th time slot and       h,L+1 () () w :L+1 = wL+1 + hk,k+1 βk Pk wm h,+1 m=1 k=m    √    hk,k+1 Pk h,L+1 () = wL+1 + wm . 2 2 Pk−1 |hk−1,k | + σw h,+1 m=1 k=m The signal received at the destination over L + 1 time slots can be collected into the vector ⎡ (0) ⎤ ⎡ (0) ⎤ ⎡ (0) ⎤ : h0,L+1 yL+1 w :L+1 ⎢ : (1) ⎥ ⎢ (1) ⎥ ⎢ (1) ⎥ ⎢ h0,L+1 ⎥ :L+1 ⎥ ⎢ yL+1 ⎥ ⎢w ⎢ ⎢ ⎥ ⎥ ⎥ (4.167) yL+1 = ⎢ ⎢ .. ⎥ = ⎢ .. ⎥ P0 xs + ⎢ .. ⎥ ⎣ . ⎦ ⎣ . ⎦ ⎣ . ⎦ (L)

yL+1

(L) : h0,L+1

= hxs + w.

(L)

w :L+1 (4.168)

By employing MRC at the destination, the effective SNR at the destination can be computed as γeff = P0 hH R−1 w h, where Rw is the covariance matrix of the effective noise vector w. Since L + 1 time slots are required for a network with L relays, the average end-to-end capacity is given by

4.6 Other Topics on Multi-Relay Cooperative Communications

187

Fig. 4.19 Outage probability for the AF cooperative multi-hop systems deploying diversity combining at the destination with various numbers of hops. (Figure regenerated from c Pavan Kumar, Bhattacharjee, Herhold and Fettweis; [2004] IEEE.)

CAF (h, Rw ) =

+ * 1 log2 det I + (hhH )R−1 w . L+1

(4.169)

Given the transmission rate R, the outage probability can be evaluated by pout = Pr (CAF (h, Rw ) < R). In Fig. 4.19 (i.e. [18, Fig. 4]), the outage probability of AF multi-hop transmissions (using diversity combining at the destination) is shown for systems with different numbers of hops. The end-to-end transmission rate is fixed as R = 1 bit/sec/Hz and the channel gains are assumed to be Rayleigh distributed with mean proportional to the path loss. We assume that the sourcedestination distance ds,d is fixed and, thus, the distance between neighboring hops decreases as ds,d /(L + 1). As the number of hops increases, the rate achievable on each hop increases due to the reduced path loss and the destination will be able to receive more copies of the signal. However, the average end-to-end rate may decrease due to the increased number of time slots required to traverse the entire path.

4.6.2 Asynchronous Cooperative Transmissions In the cooperative strategies described in previous sections, we assumed that the relays are perfectly synchronized so that their transmission arrive either

188

4 Cooperative Transmission Schemes with Multiple Relays

concurrently (e.g., in beamforming and DSTC schemes) or perfectly separated in time (e.g., in orthogonal or multi-hop transmissions) at the destination. While the performance achieved under these scenarios provide great insight on the gain achievable with cooperation, they are often difficult to realize in practice. Due to these reasons, asynchronous cooperation schemes have also received much attention in recent years, e.g., in [11,28,34]. As an example, we introduce in this subsection the opportunistic large arrays (OLA) system, where relays receive and retransmit messages in an asynchronous fashion whenever they are confident that the forwarded symbols or messages are of sufficient quality (based either on an SNR threshold or a CRC test). The source’s message propagates through the network like the chanting of ola in a sports stadium. The participants do not follow any central control but respond based only on their local observations of the signal in the environment. This scheme avoids the need for strict synchronization or coordination among cooperating terminals. Consider a network that consists of a source s, L relays (labeled 1 to L), and a destination d. To characterize the asynchronous behavior, we model the source signal as a continuous-time function x(t), which can be either an uncoded symbol or a complete codeword. After the source transmits x(t), whomever receives a signal with sufficient quality (e.g., with sufficiently high SNR) will immediately retransmit the signal to other nodes. Depending on whether DF or AF strategies are adopted, the signal forwarded by the intermediate nodes can be either a regenerated signal, say x (t) = x(t) for relay

, or an amplified version of its received signal, say x (t) = β y (t). The relays that are not able to receive with sufficiently high SNR will continue to receive from other intermediate nodes until the accumulated SNR exceeds the desired threshold. Consequently, the nodes transmit at random different instants and the signal received at relay can be written as the accumulation of signals transmitted by all other nodes. The received signal is given by  y (t) = hk, x (t − τk, ) + w (t), (4.170) k∈D

where hk, is the channel coefficient between node k and node , τk, is the arrival time of the signal transmitted by node k, and w (t) is the additive white Gaussian noise at node with correlation function Rw (τ ) = E[w (t + τ )w∗ (t)] = N0 δ(τ ). The set D is the set of nodes that eventually are able to participate in the cooperative transmission. We assume that the channel is quasi-static and flat fading so that the channel coefficients remain constant over the transmission time of the signal x(t). Interestingly, since all nodes are forwarding the same signal x(t), the signal received at relay (e.g., in (4.170)) can be treated as an equivalent multipath signal, i.e., y (t) = : hs, (t) ∗ x(t) + w : (t), (4.171)

4.6 Other Topics on Multi-Relay Cooperative Communications

189

firing instant received signal

rest phase

receive phase

delay spread time signal spatial propagation

xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx

space downstream

upstream

Fig. 4.20 Illustration of the integrate-and-fire model that describes the local node operc ations in OLA networks. (From Scaglione and Hong; [2003] IEEE.)

where : hs, (t) is the effective multipath channel and w : (t) is the effective noise. For example, in the DF case, we have  : hs, (t) = hk, δ(t − τk, ) (4.172) k∈D

and w : (t) = w (t);

(4.173)

whereas, in the AF case, we have : hs, (t) equal to the product of channel coefficients and amplifying gains of all upstream nodes, and w : (t) is noise accumulated from all upstream nodes. When linear modulation is employed, the signal x(t) can be expressed as a sequence of symbol waveforms given by x(t) =

∞ 

In gtx (t − nTs ),

n=−∞

where In = an + jbn is the complex data symbol and gtx (t) is the transmit filter. In this case, the effective multipath channel generated by the intermediate relays will result in frequency selectivity and inter-symbol interference that can be exploited either by multi-carrier modulation or standard equalization techniques. The operations at each relay can be described by an integrate-and-fire model as illustrated in Fig. 4.20, where the solid curve represents the aggregate signal received by the relay. Each relay observes the signal received from its upstream nodes until the accumulated signal yields sufficient SNR to perform reliable detection. The relay then utilizes standard equalization or RAKE receivers to exploit the multipath signal and retransmit the signal x(t) to other nodes in the network. Once the relay transmits (or “fires”), it will shut down its receiver and remain silent for the remainder of the time

190

4 Cooperative Transmission Schemes with Multiple Relays

Fig. 4.21 Fractional spaced equalizer for asynchronous cooperative systems. (Modified c from [34] [2006] IEEE.

needed for x(t) to be propagated to the destination. The rest phase is used to avoid having the same signal x(t) being received again at the same node. Specifically, a fractionally-spaced decision-feedback equalizer, as shown in Fig. 4.21, has been proposed in [34] to combine the signals received from multiple relay paths and resolve the intersymbol interference that may arise in (2) high data-rate transmissions. Here, the effective multipath signal yd received from the asynchronous relays is first passed through a T /2-fractionally spaced (1) equalizer and then combined with the signal yd received on the direct path. The diversity gains can be increased if the signals transmitted by different relays can be made more resolvable. In fact, this can be achieved artificially by employing random delays (or locally coordinated delays) at the relays. Readers are referred to [34] for further details. An alternative approach to exploit frequency selectivity is the use of multicarrier modulations. The advantage of this technique is that it can be implemented on top of many cooperative strategies proposed in this section, such as transmit beamforming, selective relaying, or DSTC etc. These issues will be discussed in detail in Chapter 7. Issues regarding asynchronous relays in DSTC systems can also be found in [20–22, 32, 38]. Further studies on the diversity-multiplexing tradeoff of asynchronous cooperative systems can be found in [33] and issues regarding channel and delay estimation for asynchronous cooperation are given in [30].

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22. Li, Z., Xia, X.-G.: A simple Alamouti space-time transmission scheme for asynchronous cooperative systems. IEEE Signal Processing Letters 14(11), 804–807 (2007) 23. Luo, L., Blum, R.S., Cimini, L.J., Greenstein, L.J., Haimovich, A.M.: Decode-andforward cooperative diversity with power allocation in wireless networks. (There’re two paper with the identical names, conference and journal versions: In: Proceedings of the IEEE GLOBECOM, 5, pp. 3048-3052 (2005) IEEE Transactions on Wireless Communications 6(3), 793-799 (2007) ) 24. Madan, R., Mehta, N., Molisch, A., Zhang, J.: Energy-efficient cooperative relaying over fading channels with simple relay selection. IEEE Transactions on Wireless Communications 7(8), 3013–3025 (2008) 25. Mudumbai, R., Hespanha, J., Madhow, U., Barriac, G.: Distributed transmit beamforming using feedback control. IEEE Transactions on Information Theory 56(1), 411–426 (2010) 26. Mudumbai, R., Brown III, D.R., Madhow, U., Poor, H.V.: Distributed transmit beamforming: challenges and recent progress. IEEE Communications Magazine 47(2), 102– 110 (2009) 27. Pun, M.-O., Brown III, D.R., Poor, H.V.: Opportunistic collaborative beamforming with one-bit feedback. In: Proceedings of the IEEE 9th Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2008) 28. Scaglione, A., Hong, Y.-W.: Opportunistic large arrays: Cooperative transmission in wireless multihop ad hoc networks to reach far distances. IEEE Transactions on Signal Processing 51(8), 2082–2092 (2003) 29. Si, J., Li, Z., Liu, Z., Lu, X.: Joint route and power allocation in cooperative-multihop networks. In: Proceedings of the IEEE international conference on circuits and systems for communications, pp. 114–118 (2008) 30. Tourki, K., Deneire, L.: Channel and delay estimation algorithm for asynchronous cooperative diversity. Wireless Personal Comunications 37, 361–369 (2006) 31. Vandenberghe, L., Boyd, S.: Semidefinite programming. SIAM review 38(1), 49–95 (1996) 32. Wang, D., Fu, S.: Asynchronous cooperative communications with STBC coded single carrier block transmission. In: Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), pp. 2987–2991 (2007) 33. Wei, S.: Diversity-multiplexing tradeoff of asynchronous cooperative diversity in wireless networks. IEEE Transactions on Information Theory 53(11), 4150–4172 (2007) 34. Wei, S., Goeckel, D.L., Valenti, M.: Asynchronous cooperative diversity. IEEE Transactions on Wireless Communications 5(6), 1547–1557 (2006) 35. Yi, Z., Kim, I.-M.: Joint optimization of relay-precoders and decoders with partial channel side information in cooperative networks. IEEE Journal on Selected Areas in Communications 25(2), 447–458 (2007) 36. Yiu, S., Schober, R., Lampe, L.: Distributed space-time block coding. IEEE Transactions on Communications 54(7), 1195–1206 (2006) 37. Yiu, S., Schober, R., Lampe, L.: Decentralized distributed space-time trellis coding. IEEE Transactions on Wireless Communications 6(11), 3985–3993 (2007) 38. Yu, Q., Zheng, J., Fu, T., Wu, K., Zhang, B.: Asynchronous cooperative transmission using distributed unitary space-frequency coded OFDM in mobile ad hoc networks. In: IEEE Future Generation Communication and Networking, vol. 2, pp. 291–296 (2007) 39. Zhao, Q., Tong, L.: Opportunistic carrier sensing for energy-efficient information retrieval in sensor networks. EURASIP Journal on Wireless Communications and Networking 2005(2), 231–241 (2005) 40. Zhao, Y., Adve, R., Lim, T.: Beamforming with limited feedback in amplify-andforward cooperative networks. In: Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), pp. 3457–3461 (2007) 41. Zhao, Y., Adve, R., Lim, T.J.: Improving amplify-and-forward relay networks: optimal power allocation versus selection. IEEE Transactions on Wireless Communications 6(8), 3114–3123 (2007)

Chapter 5

Fundamental Limits of Cooperative and Relay Networks

In this chapter, the fundamental limits of relay networks are introduced under different channel settings, including Gaussian channels and wireless fading channels. For each scenario, we will study the information-theoretic capacities, diversity-multiplexing tradeoffs, and scaling laws of large networks accordingly. In Section 5.1, we first examine the case of the single-relay Gaussian channel. When the relay is full-duplex, it can be shown that decodeand-forward (DF) and compress-and-forward (CF) schemes achieve capacity under certain scenarios; however, such results are not enjoyed in the more practical half-duplex relay network. In Section 5.2, we take the channel fading into consideration and discuss the fundamental limits under fast and slow fading scenarios. It will be shown that the DF protocol achieves ergodic capacity in fast fading channel while the dynamic decode-and-forward (DDF) protocol achieves the optimal diversity-multiplexing tradeoff (DMT) when the multiplexing gain is smaller than one-half. In the final section, we extend the information-theoretical results to networks with more than one relay. It can be shown that amplify-and-Forward (AF) is close to optimal (i.e., near capacity-achieving) in Gaussian channels when the number of relays is large. The DF and DDF protocols are also optimal for fast and slow fading channels under certain circumstances. Many of the results for multi-relay systems follow from the properties of the relay channel as well as that of conventional MIMO channels.

5.1 Gaussian Relay Channels The basic element of a cooperative system is a single relay system with one source, one relay, and one destination. This model was first introduced in the pioneering work by van der Meulen in [7]. In this section, we will focus on the Gaussian single-relay channel where the relay and destination suffer from Gaussian noise. Although the general characterization of the relay channel

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_5, © Springer Science+Business Media, LLC 2010

193

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5 Fundamental Limits of Cooperative and Relay Networks

Fig. 5.1 General single relay channel.

capacity is not known up to this day, meaningful achievable regions have been derived as we describe in the following. We will also show some special cases of which the capacities are known.

5.1.1 Cut-Set Bound of Gaussian Relay Channels We first introduce the model for general single relay channels of which Gaussian relay channels are included. Consider the relay channel illustrated in Figure 5.1 where there is one source, one destination, and a single intermediate node that relays information from the source to the destination. The channel input Xns = [Xs [1], Xs [2], . . . , Xs [n]] of length n is selected according to the message u, and satisfies the power constraint 1 ( n E Xs n

2

)

2 ) 1  ( = E Xs [i] ≤ Ps . n i=1 n

Let Yrn = [Yr [1], . . . , Yr [n]] be the observations made by the relay and let Xnr = [Xr [1], . . . , Xr [n]] be the signals produced by the relay based on these observations. The relay signals are functions of the past local observations, that is, Xr [i] = fr,i (Yr [i − 1], Yr [i − 2], . . . , Yr [1]), and satisfy the power constraint as 1 ( n E Xr n i=1 n

2

) =

n 2 ) 1  ( E Xr [i] ≤ Pr . n i=1

Let xs , xr , yr , and yd be the realizations of Xs , Xr , Yr and Yd , respectively. The memoryless relay channel is specified by p(yr , yd |xs , xr ), the conditional probability that yr and ys are observed given that xs and xr were transmitted. The considered channel is memoryless in the sense that (yr [i], yd [i]) depends on the past channel inputs {(xs [j], xr [j])}ij=1 only through (xs [i], xr [i]). That is, for any message u ∈ U, where U is the set of possible messages, any choice of codewords xns = [xs [1], · · · , xs [n]], and any relay code functions {fr,i }ni=1 ,

5.1 Gaussian Relay Channels

195

we have the joint probability mass function   * + * + p yr [i], yd [i]u, xis , xir , yri−1 , ydi−1 = p yr [i], yd [i]xs [i], xr [i] , where xis = [xs [1], xs [2], · · · , xs [i]], xir = [xr [1], · · · , xr [i]], yri−1 = [yr [1], · · · , yr [i − 1]], and ydi−1 = [yd [1], · · · , yd [i − 1]]. Besides, there are two kinds of relay devices, that is, the full-duplex and half-duplex relays. The full-duplex relays are those that can transmit and receive at the same time and in the same frequency band, while the half-duplex relays cannot transmit and receive at the same time. The relays might be fullduplex if each relay has two antennas: one receiving and one transmitting. However, due to practical considerations such as the dynamic range of incoming and outgoing signals and the bulk of ferroelectric components like circulators, most cooperative protocols proposed in the literature (and also in Chapters 3 and 4) impose the half-duplex constraint on the relays. For half-duplex relays, this additional constraint is yr [i] = 0 if xr [i] = 0 ∀i.

(5.1)

Based on the observations, the destination computes an estimate of the message u ˆ. Let U be the message set and |U| is the cardinality of U, the rate of a length n code log2 |U| R= n is said to be achievable if for any  > 0, there exists sufficiently large n such that Pr(ˆ u = u) < . The capacity C is the supremum of the set of achievable rates. In this section, we will focus on Gaussian relay channels. Specifically, as the source broadcast its symbol through the Gaussian relay channel, the signal received at the relay and the destination can be written as Yr [i] = Xs [i] + Wr [i]

(5.2)

Yd [i] = Xs [i] + Xr [i] + Wd [i],

(5.3)

and respectively, where Wrn = [Wr [1], · · · , Wr [n]] consists of i.i.d. zero-mean Gaussian random variables with variance σr2 , i.e., Wr [i] ∼ N (0, σr2 ), and Wdn = [Wd [1], · · · , Wd [n]] consists of i.i.d. random variables with distribution N (0, σd2 ) and is independent of Wrn . It is easy to see that this channel belongs to the general relay channel described in above. Suppose that the relay is full-duplex, the cut-set bound is introduced for the full-duplex Gaussian relay channel to characterize a capacity upper bound.

196

5 Fundamental Limits of Cooperative and Relay Networks

Fig. 5.2 Relay channel with broadcast cut and multiple-access cut.

Theorem 5.1. The capacity of the Gaussian relay channel Cf with fullduplex relay is upper-bounded by Cf ≤

sup min{I(Xs , Xr ; Yd ), I(Xs ; Yr , Yd |Xr )},

(5.4)

p(xs ,xr )

where the supremum is over all jointly Gaussian distributions p(xs , xr ) that satisfy the power constraints Ps and Pr , respectively. This theorem comes from the concept of max-flow min-cut. The information rate (flow) across any boundary (cut) is less than the mutual information between the inputs on one side of the boundary and the outputs on the other side, conditioned on the inputs on the other side. From Figure 5.2, the term I(Xs , Xr ; Yd ) comes from the multiple-access cut and the term I(Xs ; Yr |Xr ) comes from the broadcast cut. And the information rate must be smaller than the flow in the minimum cut. With proper modifications, this theorem can be easily applied to other channels, including the discrete memoryless channel (DMC). The details of the proof can be found in [3]. Extensions to networks with more than one relay nodes can be found in Section 5.3.1. As for the half-duplex Gaussian relay channel, without loss of generality, we assume that the relay is in the receive mode for a κn symbols and in the transmit mode for the rest. Let the superscripts “1” represent the parameters in the relay-receive mode, and the superscripts “2” represent the parameters in the relay-transmit mode. The capacity can be upper-bounded as given in the following theorem. Theorem 5.2. The capacity of the Gaussian relay channel Ch with halfduplex relay is upper-bounded by Ch ≤

sup

(1)

(2)

min{κI(Xs(1) ; Yd |Xr = 0) + (1 − κ)I(Xs(2) , Xr ; Yd ),

(1) (2) p(xs ,xs ,xr )

(1)

(2)

κI(Xs(1) ; Yr(1) , Yd |Xr = 0) + (1 − κ)I(Xs(2) ; Yd |Xr )}, (1)

(2)

where p(xs , xs , xr ) is the joint Gaussian distribution function that satisfies the power constraints Ps and Pr imposed on the source and relay, respectively. The proof of Theorem 5.2 is a simple extension of that of Theorem 5.1 with the half-duplex constraint in (5.1) [4].

5.1 Gaussian Relay Channels

197

5.1.2 Decode-and-Forward and Degraded Relay Channels The first important achievable rate of relay channels is developed for the decode-and-forward protocol [2], which is tight for physically degraded discrete memoryless relay channels and some Gaussian relay channels with fullduplex relays. After decoding the message, the relay re-encodes the message and transmits a new codeword. For full-duplex relay, the achievable rate is Theorem 5.3. Suppose that the relay complies with the decode-and-forward protocol, the capacity of the full-duplex Gaussian relay channel is lowerbounded as Cf ≥

sup min{I(Xs , Xr ; Yd ), I(Xs ; Yr |Xr )},

(5.5)

p(xs ,xr )

where the supremum is taken over the joint Gaussian distribution function p(xs , xr ) that satisfies the power constraints Ps and Pr . Here we provide the basic concept of the proof. Suppose that the source uses block Markov coding scheme, that is, the codeword for block j depends on the current message uj and the previous message uj−1 . At the end of block j, when decoding message uj from the received signal at the relay yrn (j), the relay has past estimations of u1 , ..., uj−1 and the correct xnr (j − 1). The rate R must be less than I(Xs ; Yr |Xr ) to make decoding at relay correct. The relay encodes uj−1 into xnr (j) by using another codebook and retransmits it to the destination. With the information of correctly decoded xnr (j), the message uj−1 can be known at the destination. The channel is similar to a 2 × 1 MISO channel and the rate R must be less than I(Xs , Xr ; Yd ) to ensure that the destination can correctly decode the data. The source and the relay act as a collocated multiple antenna system. The details can be found in [3]. In the following, we will show that decode-and-forward is optimal for fullduplex degraded relay channel. To begin with, the degraded relay channel is defined below. Definition 5.1 (Degraded Relay Channel). The relay channel is degraded if p(yr , yd |xs , xr ) = p(yr |xs , xr )p(yd |yr , xr ). (5.6) The degraded relay channel defined above can be interpreted as the case where the destination receives a combination of the degraded version of the signal observed at the relay Yr , and the output of the relay Xr . More specifically, the relay channel is degraded if p(yd |xs , xr , yr ) = p(yd |xr , yr ), i.e., Xs → (Xr , Yr ) → Yd is a Markov chain. Due to this Markov chain, the upper bound in Theorem 5.1 and decode-and-forward lower bound in Theorem 5.3 coincide [2], then the capacity of the degraded relay channel is given in the following theorem.

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5 Fundamental Limits of Cooperative and Relay Networks

Fig. 5.3 Degraded Gaussian relay channel.

Theorem 5.4. The capacity of the degraded Gaussian relay channel with fullduplex relay is Cdeg =

sup min{I(Xs , Xr ; Yd ), I(Xs ; Yr |Xr )}

(5.7)

p(xs ,xr )

where the supremum is taken over the joint Gaussian distribution p(xs , xr ) that satisfies the power constraints Ps and Pr . Let us take a closer look at the degraded Gaussian relay channel as illustrated in Figure 5.3. Specifically, the observations made by the relay and the destination are Yr [i] = Xs [i] + Wr [i] (5.8) and Yd [i] = Yr [i] + Xr [i] + Wd [i] = Xs [i] + Xr [i] + Wr [i] + Wd [i]

(5.9)

where Wrn = [Wr [1], . . . , Wr [n]] consists of i.i.d. zero-mean Gaussian random variables with variance σr2 , i.e., Wr [i] ∼ N (0, σr2 ), and Wdn = [Wd [1], . . . , Wd [n]] consists of i.i.d. random variables with distribution N (0, σd2 ) and is independent of Wrn . The formula in Theorem 5.4 can be explicitly expressed as the following. Theorem 5.5. The capacity of the real degraded Gaussian relay channel is √    0

Ps +Pr +2β Ps Pr 1 (1−β 2 )Ps 1 Cdeg = max min log2 1+ , log2 1+ . 0≤β≤1 2 σr2 +σd2 2 σr2 Similarly to Theorem 5.3, the first term is obtained by having the source and the relay transmit coherently over the Gaussian channel and the second term is to guarantee correct decoding of the source’s message at the relay. The parameter β can be treated as the correlation between Xs and Xr to specify the joint Gaussian distribution. According to the SNR of the source-relay link and relay-destination link, the results can be discussed by considering two different cases. Case I:

For Ps /σr2 ≥ Pr /σd2 , we can show that

5.1 Gaussian Relay Channels

199

Fig. 5.4 General relay channel with feedback.

Ps + Pr + 2 (1 − β)Ps Pr βPs Ps Ps + Pr − ≤ 2 − 2 σr2 σr2 + σd2 σr σr + σd2 σ2

Ps (1 + σd2 ) Ps r ≤ 2 − = 0, σr σr2 + σd2 where the first inequality results from the fact that β = 1 maximizes the left-hand-side of it. Hence, the capacity is given by   Ps 1 Cdeg = log2 1 + 2 . 2 σr This is an improvement compared to the case with no relay, which yields   Ps 1 Cno relay = log2 1 + 2 . 2 σr + σd2 Case II: For Ps /σr2 < Pr /σd2 , the capacity is maximized by choosing β ∗ such that     Ps + Pr + 2 (1 − β)Ps Pr 1 βPs 1 = log2 1 + 2 . log2 1 + 2 σr2 + σd2 2 σr In this case, the capacity becomes Cdeg =

  β ∗ Ps 1 log2 1 + 2 . 2 σr

Note that the capacity result in Theorem 5.4 can be easily applied to other degraded relay channels, with continuous or finite alphabets. The followings are two examples. Example 1: General Relay Channel with Feedback A general relay channel with feedback, as shown in Figure 5.4, can be considered as a special case of the degraded relay channel. More specifically, in this case, the relay observes (Yr , Yd ) which contains the observation Yd

200

5 Fundamental Limits of Cooperative and Relay Networks

made at the destination. Hence, by replacing Yr with (Yr , Yd ) in (5.7), we obtain the capacity of the general relay channel with feedback as sup min{I(Xs , Xr ; Yd ), I(Xs ; Yr , Yd |Xr )},

C=

(5.10)

p(xs ,xr )

where p(xs , xr ) is the joint distribution of Xs , Xr which satisfies the power constraints. Example 2: Reversely Degraded Relay Channel The reversely degraded relay channel presents the channel where the relay observes a degraded version of the observation made by the destination. The formal definition is given as the following. Definition 5.2 (Discrete Reversely Degraded Relay Channel). The relay channel defined over finite alphabets (Xs × Xr , p(yr , yd |xs , xr ), Yr × Yd ) is reversely degraded if p(yr , yd |xs , xr ) = p(yd |xs , xr )p(yr |yd , xr ).

(5.11)

In this case, the relay will not be able to cooperate in forwarding the source’s message, but may transmit a symbol xr to facilitate the source’s transmission. Given that Xr [i] = xr , for all i, the capacity of the reversely degraded relay channel with discrete alphabet follows directly from the results of the pointto-point channel and is given by Crev = max max I(Xs ; Yd |xr ), xr ∈Xr p(xs )

where Xr is the finite alphabets of Xr and p(xs ) is the distribution of xs drawing from finite alphabets Xs . As for the half-duplex relay, the relay spends a fraction of κn symbol time to listen (Phase 1), and the rest to transmit (Phase 2). Moreover, the source can split the message into two parts to improve the performance, one part is transmitted directly to the destination without the help of the relay, and the other part is transmitted through the relay. This scheme is sometimes called as “partial decode-and-forward” since only part of the source message is decoded by the relay. The achievable rate is lower-bounded as follows. Theorem 5.6. The capacity of the Gaussian relay channel Ch with halfduplex relay is lower-bounded by Ch ≥

max

(1) (2) p(xs ,xs ,xr )

(1)

(2)

min{κI(Xs(1) ; Yd |Xr = 0) + (1 − κ)I(Xs(2) , Xr ; Yd ), (2)

κI(Xs(1) ; Yr(1) |Xr = 0) + (1 − κ)I(Xs(2) ; Yd |Xr )}, (5.12)

5.1 Gaussian Relay Channels (1)

201

(2)

where p(xs , xs , xr ) is the jointly Gaussian distribution which satisfies the power constraints Ps and Pr , respectively. On transmitting a message u, the message is split into two parts, say ur and ud , where the message ur is the message to be decoded by the relay (with rate Rr ), and ud is the one being direct transmitted to the destination (with rate Rd ), respectively. The overall rate is R = Rr + Rd . The basic concept is described as follows, while the details can be found in [4]. In Phase 1, the relay-receive mode, the source transmits ur for the relay to decode. And in Phase 2, the relay transmits ur while the source transmits ur and ud using the superposition coding. The channel at destination during Phase 2 is a two-user MAC channel. One virtual user has message ud , which is encoded (2),n in codeword xs , while the other user has message ur , which is encoded in n codeword xr . For message ud , if (2)

Rd < (1 − κ)I(Xs(2) ; Yd |Xr )

(5.13)

in Phase 2, it can be decoded correctly at the destination. Since ur is transmitted in Phase 1 (without cooperation) and Phase 2 (with cooperation), if (1) (2) Rr < κI(Xs(1) ; Yd |Xr = 0) + (1 − κ)I(Xr ; Yd ) (5.14) it can be decoded correctly at the destination. However, ur must be decoded correctly at the relay in Phase 1, which can be done if Rr < κI(Xs(1) ; Yr(1) |Xr = 0).

(5.15)

By adding (5.13) and (5.14) we get the first term in the brackets in (5.12), while by adding (5.13) and (5.15) we get the second term in the brackets in (5.12), respectively.

5.1.3 Compress-and-Forward For the degraded relay channel, where the relay observes a signal with higher quality compared to the destination, the results given in Theorem 5.4 implies that the source and the relay should cooperate fully to transmit the message u. On the other hand, when the relay observes instead a degraded version (more noisy) of the signal observed at the destination (i.e., the reversely degraded relay channel), the relay should choose the best Xr to transmit in order to facilitate the source’s transmission. Both cooperation and facilitation yield achievable rate regions for the general relay channel but are attained by considering two rather extreme scenarios. However, even though the relay may observe a less informative signal, the observation may not be a degraded version of the output observed at the destination. In this case, an interme-

202

5 Fundamental Limits of Cooperative and Relay Networks

diate strategy often referred to as the compress-and-forward strategy [5] (or estimate-and-forward in [2]) which has been described briefly in Chapter 3 and may be used to derive a better achievable rate in some cases. Specifically, in the case where the relay may not be able to completely decode the message transmitted by the source, full cooperation will not be possible. Yet, the observation Yr still contains useful information regarding the message u and should be forwarded to the destination. However, with limited channel capacity between the relay and the destination, the observation Yr cannot be forwarded with infinite precision, but an estimate Yˆr may be transmitted instead. The achievable rate is given in the following theorem. Theorem 5.7. Suppose that the full-duplex relay complies with compressand-forward, the capacity is lower-bound as Cf >

sup

I(Xs ; Yd , Yˆr |Xr ),

p(xs ,xr ,yd ,yr ,ˆ yr )

and the lower bound is achievable subject to the constraint that I(Xr ; Yd ) ≥ I(Yr ; Yˆr |Xr , Yd ).

(5.16)

The supremum is taken over the joint Gaussian distribution of the form p(xs , xr , yd , yr , yˆr ) = p(xs )p(xr )p(yd , yr |xs , xr )p(ˆ yr |yr , xr ), which satisfies the power constraints. Theorem 5.7 also illustrates the multi-antenna reception behavior: the compress-and-forward rate I(Xs ; Yd , Yˆr |Xr ) can be interpreted as the rate for a 1×2 SIMO channel (one transmit antenna, two receive antennas) as long as the rate constraint is satisfied. The results obtained above can be interpreted as follows. Suppose that the destination is able to decode reliably the message transmitted by the relay and, thus, the relay’s transmitted signal Xr is assumed known at the destination. The signal Xr contains information regarding the estimate Yˆr . Suppose that Yd and Yˆr are observed at the destination, the rate achievable in the destination is given by I(Xs ; Yd , Yˆr |Xr ). The transmission is enabled by: 1) compressing Yr into the rate R0 = I(Yr ; Yˆr |Xr , Yd ) when Xr and Yd is available at the destination; 2) conveying the compressed information to the destination under the constraint that R0 does not exceed the mutual information between Xr and Yd , that is, R0 ≤ I(Xr ; Yd ). The compression can be done based on the results of source coding with side information [11]. While the source-destination pair incorporating a half-duplex relay that complies with the compress-and-forward protocol, similar to the decode-andforward protocol in Theorem 5.6, we can also combine the rate-splitting

5.2 Single-Relay Fading Channels

203

technique with the compress-and-forward protocol. One part of message is transmitted directly to the destination, and the other part is helped by the relay via the source coding with side information. The achievable rate of this two-phase transmission is quite complex and the readers can refer to [4] for the detail formulae. Note that unlike decode-and-forward, choosing jointly Gaussian distribution may not be the optimal distribution for the compressand-forward even in the Gaussian relay channel.

5.2 Single-Relay Fading Channels In wireless fading channels, with the help of a single relay, the received signals at the relay and the destination are given by Yd [i] = hs,d [i]Xs [i] + hr,d [i]Xr [i] + Wd [i],

(5.17)

Yr [i] = hs,r [i]Xs [i] + Wr [i], where hs,d [i] is the channel from source to destination, hr,d [i] is the channel from relay to destination, and hs,r [i] is the channel from source to relay at time slot i, respectively. The noise vectors Wdn = [Wd [1], · · · , Wd [n]] and Wrn = [Wr [1], · · · , Wr [n]] consist of i.i.d. circularly symmetric Gaussian random variables. Typically, there are two kinds of fading channels considered in the literature, the slow and fast fading channels. In the fast fading channels each channel gain is assumed to be an i.i.d. random process with respect to time index; while in the slow fading channels, the channel gains are random but fixed within one codeword length. In both cases, the channel gains can be obtained perfectly at the receiver by assuming perfect channel estimation. However, due to the limited feedback channel bandwidth from the receiver to the transmitter, these channel coefficients are hard to be known perfectly at the transmitter.

5.2.1 Ergodic Capacity In this section, we will discuss the fast fading relay channels with partial channel state information at the transmitter. Before that, let us focus on a simple case where channel gains remain the same during each codeword length, and the transmitter knows the channel gains perfectly. The results of this channel is very useful for understanding the capacity in the fast fading channel. By taking the distance between nodes into consideration, we set 1 hs,d =  , dα s,d

204

5 Fundamental Limits of Cooperative and Relay Networks

where ds,d is the distance between source and distinction and α is an attenuation exponent of path loss effect. Similarly, hs,r = 1/( dα s,r ) and  α hr,d = 1/( dr,d ). For this channel, the result of section 5.1.1 can be directly applied as follows. Let the correlation coefficient between Xs and Xr in Theorem 5.1 be β, the cut-set bound of the full-duplex relay channel is  ,  1 1 2 Cf ≤ max min log2 1 + Ps ( α 2 + α 2 )(1 − |β| ) , (5.18) 0≤β≤1 ds,r σr ds,d σd  ? √ Ps Pr 2β Ps Pr . log2 1 + α 2 + α 2 + α/2 α/2 ds,d σd dr,d σd ds,d dr,d σd2 Similarly, the best decode-and-forward rate from Theorem 5.3 is ,   Ps Rdf = max min log2 1 + α 2 (1 − |β|2 ) , (5.19) 0≤β≤1 ds,r σr  ? √ Ps Pr 2β Ps Pr . log2 1 + α 2 + α 2 + α/2 α/2 ds,d σd dr,d σd d d σ2 s,d

r,d

d

As for the compress-and-forward scheme, we use the sub-optimal setting by selecting all variables as Gaussian, and the Gaussian test channel [3] Yˆr = ˆ r where the compression error W ˆ r is assumed to be a zero-mean Yr + W ˆr and independent of all complex Gaussian random variable with variance N other random variables. The rate in Theorem 5.7 is then given by   Ps Ps Rcf = log2 1 + (5.20) + α 2 , 2 ˆ ds,d σd dα s,r σr (1 + Nr ) with ˆr = N

Ps 2 dα s,r σr

+

Ps 2 dα s,d σd

Pr 2‘ dα r,d σd

+1 .

Now suppose the source, relay, and destination are aligned as in Figure 5.5, where ds,r = |d|, dr,d = |1 − d| and ds,d = 1. As the relay moves toward the source d → 0, Rdf coincides with the cut-set bound. The limiting capacity is √   Ps Pr 2 Ps Pr lim Cf = log2 1 + 2 + 2 + . d→0 σd σd σd2 While the relay moves toward the destination d → 1, Rcf coincides with the cut-set bound. The limiting capacity is    1 1 lim Cf = log2 1 + Ps . + 2 d→1 σr2 σd

5.2 Single-Relay Fading Channels

205

Fig. 5.5 Single relay on a line. (Figure regenerated and modified from Kramer, Gastpar, c and Gupta. 2005 IEEE.)

Now we consider the fast-fading channel with phase fading, that is, each channel gain has an random phase unknown to the transmitter as ejθs,d [i] hs,d [i] =  , dα s,d where θs,d [i] is i.i.d in the time domain and uniformly distributed in [0, 2π). The hs,r and hr,d are defined similarly. All random phases of the three nodes are independent of each other. Under the above channel setting, we have the following capacity result. Theorem 5.8. When the relay is close to the source such that Ps α ds,d σd2

+

Pr α dr,d σd2

≤

Ps , α ds,r σr2

(5.21)

the ergodic capacity for full-duplex relay channel is   Ps Pr Cf = log2 1 + α 2 + α 2 . ds,d σd dr,d σd And the capacity is achieved by utilizing the decode-and-forward protocol. Proof. In fast-fading channel, the rate E[Rdf ] is achievable [5], where the expectation is over the random phases. This rate becomes 

 0 Ps max min log2 1 + α 2 (1 − |β|2 ) , R(β) , (5.22) 0≤β≤1 ds,r σr where  R(β) = Eφs,d ,φr,d log2



 √ Ps Pr 2Re(βej(φs,d −φr,d ) ) Ps Pr 1+ α 2 + α 2 + . α/2 α/2 ds,d σd dr,d σd ds,d dr,d σd2

From Jensen’s inequality [3],

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5 Fundamental Limits of Cooperative and Relay Networks

Fig. 5.6 Rate comparisons of cooperative schemes in three-node fast fading relay channels. c (From Kramer, Gastpar, and Gupta with modified labels. 2005 IEEE.)

  R(β) ≤ log2 E

 √ Ps Pr 2Re(βej(φs,d −φr,d ) ) Ps Pr 1+ α 2 + α 2 + . α/2 α/2 ds,d σd dr,d σd ds,d dr,d σd2

It is easy to see that the right-hand-side of above equation equals to R(0), and R(β) ≤ R(0) (5.23) With (5.23) and (5.21), it is easy to check that the decode-and-forward achievable rate (5.22) is maximized at β = 0. From the same arguments, the cut-set bound is also maximized at β = 0 [5]. The capacity upper and lower bounds are (5.18) and (5.22) with β = 0 and they coincide. This concludes the proof.  The ergodic capacity results can be extended to other fading processes when the channel phase uniformly distributed over time and [0, 2π), such as Rayleigh fading channels. Figure 5.6 plots the rates for the geometry of Figure 5.5 with α = 2 and Ps = Pr = 10. It also shows the rates of compress-andforward where the relay uses the same test channel as that for the no-fading case. The achievable rate using compress-and-forward is E[Rcf ] = Rcf as (5.20). Note that the compress-and-forward also matches the upper cut-set bound in some cases, and has good performance for all d. The “relay off” curve is the case where relay does not transmit and Pr = 0. The total power consumption is only half of that being used in other curves. However, the ergodic capacity results in Theorem 5.8 can not be extended to the half-duplex relay channels. Only some partial results can be obtained. Interested readers are referred to [4] [5] for these results.

5.2 Single-Relay Fading Channels

207

5.2.2 Diversity and Multiplexing Tradeoffs For the slow fading channel, or the quasi-static channel, the channel gains are fixed during one codeword interval. When the channel gains are not known at the transmitter, the outage probability is a good performance metric. However, since relay channels can be treated as virtual MIMO channels, the more advanced diversity and multiplexing tradeoff (DMT) developed originally for MIMO channels is worth to study. This tradeoff studies the fundamental tradeoff between the achievable diversity and multiplexing gains for a given number of spatial dimensions. The more relays participating in the cooperation, the more spatial dimensions can be exploited to enhance system performance. We will use the DMT as the performance metric here, with the background provided in Section 2.3. The performance results based on outage probabilities can be derived easily from the non-fading case (or slow-fading channels with full channel state information at the transmitter) provided in Section 5.2.1. Interested readers are referred Chapter 3 and [4] [5] for the details. Achieving the optimal DMT for the full-duplex relay channel is rather straightforward, for example, one can use the simple amplify-and-forward (AF) strategy in [1]. We then focus on the channel with a half-duplex relay. The half-duplex constraint is often the cause of spectral inefficiency in the cooperative protocol. The protocols designed based on half-duplex relays, such as the DF and AF protocols are initially proposed in [6] and further improved by [1]. In the DF cooperative network where the relay first decodes the messages and then transmits a new codeword to the destination, the achievable DMT has been shown to outperform the one in the AF cooperative network. Indeed, the dynamic decode-and-forward (DDF) protocol achieves the optimal tradeoff for a certain range of multiplexing gains. In the following, we will first introduce the AF protocol and then the other complex DF protocols. Moreover, we study the DMT for those relay protocols. Let us review our channel model and introduce a general DMT upper bound. We consider the simple three-node cooperative network with a source, a relay, and a destination, each equipped with a single antenna as (5.17). The channels gains are assumed to be Exponentially distributed and known perfectly only at the corresponding receivers. For simplicity, the power constraints of source and relay are both set to P . For this channel, we have the following upper bound. Theorem 5.9. The optimal diversity gain for the three-node cooperative network (5.17) with a single relay is upper-bounded by d∗ (r) ≤ 2(1 − r), where 0 ≤ r ≤ 1 is the multiplexing gain.

208

5 Fundamental Limits of Cooperative and Relay Networks

The definitions of the diversity gain and multiplexing gain can be found in Section 2.3. This genie-aided upper bound is obtained when the relay is assumed to know the information message a priori, and the bound follows directly from [12].

DMT for AF Cooperative Networks We will introduce the nonorthogonal amplify-and-forward (NAF) protocol, which achieves the best DMT by the class of AF protocols. Under the halfduplex constraint, the AF cooperation must be divided into two phases. In Phase 1, the source first transmits a codeword of length n to both the relay and the destination; while, in Phase 2, the relay amplifies and forwards the message concurrently with the source in the following n − n symbol periods. From the channel model (5.17), the signal received at the destination over the two phases can be written as     0 hs,d A1 0 ¯s + yd = wr + wd , x (5.24) hr,d hs,r BA1 hs,d A2 hr,d B 

¯ s ∈ Cn is the length-n complex source symbols, wr ∈ Cn is the where x AWGN at the relay in Phase 1, and wd ∈ Cn is the AWGN at the destination   over both phases. Moreover, B ∈ C(n−n )×n is the linear AF matrix at the     relay and A1 ∈ Cn ×n , A2 ∈ C(n−n )×(n−n ) are diagonal power allocation matrices at the source in Phase 1 and 2, respectively. These matrices are chosen to satisfy the average power constraint at the relay 

|hs,r | P 2

n 



|bji | |ai | + 2

i=1

2

σr2

n 

|bji |2 ≤ P,

j = 1, . . . , n − n ,

(5.25)

i=1

where the (j, i)-th entry of matrix B is bji and A1 = diag(a1 , . . . , an ). The NAF protocol chooses n = n/2, A1 = I n2 × n2 , A2 = I n2 × n2 , and with b ≤



B = bI n2 × n2 P P |hs,r |2 +σr2 .

The protocol is nonorthogonal compared with the

Laneman-Tse-Wornell AF (LTW-AF) protocol proposed in [6] since the latter chooses A1 = I n2 × n2 , A2 = 0. LTW-AF is equivalent to the basic AF introduced in Section 3.2. And we will see that this relaxation of orthogonality improves the performance a lot. The upper bound of DMT for all AF networks was derived in [1] and is given as follows.

5.2 Single-Relay Fading Channels

209

Theorem 5.10. The optimal diversity gain for a cooperative network with a single AF relay is upper-bounded by d∗ (r) ≤ (1 − r) + (1 − 2r)+ , where function (x)+ = max{x, 0}. This upper bound can be achieved with the NAF protocol as follows. Theorem 5.11. The NAF protocol achieves the optimal DMT for the AF single-relay scenario as d∗ (r) = (1 − r) + (1 − 2r)+ . Proof. To derive the diversity order of the NAF protocol, we first derive an asymptotic upper bound for the error probability that is averaged over the ensemble of Gaussian random codebooks by the ML decoder. It then follows that there must exists at least one codebook with error performance bounded by this average upper bound. And the diversity order achievable by this codebook is at least as good as the exponential order of the average upper bound. Suppose that the source uses a Gaussian codebook with codewords of length-n (n is taken to be even), and rate R = r log2 ρ, where ρ = P/σd2 is the SNR. We use the notation ·

f (ρ) ≤ g(ρ) to indicate that limρ→∞

log2 f (ρ) log2 g(ρ)

(5.26)

≤ 1, and the notation ·

f (ρ) = ρ−v

(5.27)

log2 f (ρ) = −v. log2 ρ The average error probability of the ML decoder, i.e., Pe (ρ), can be upper bounded as to represent the fact that lim

ρ→∞

Pe (ρ) = PO (R)Pe|O + Pe,Oc ≤ PO (R) + Pe,Oc ,

(5.28)

where O denotes the outage event. We will choose O to make Pe,Oc being dominated by PO (R). To derive the probability Pe,Oc , we first focus on the conditional ML pairwise error probability given the channels, which can be bounded by [1] ·

Pe|hs,d ,hr,d ,hs,r ≤ ρ− 2

n

max{2(1−vs,d ),1−(vr,d +vs,r ),0}

· ρrn ,

210

5 Fundamental Limits of Cooperative and Relay Networks ·

for (vs,d , vr,d , vs,r ) ∈ R3+ , where ≤ is defined in (5.26) and we rewrite channel gains with respect to the SNR as |hs,d |2 = ρ−vs,d , |hr,d |2 = ρ−vr,d , and |hs,r |2 = ρ−vs,r . Then the error probability averaged over the set of channel realizations that do not cause an outage (i.e., Oc ) is given by  4 n 5 · ρ− 2 max{2(1−vs,d ),1−(vr,d +vs,r ),0} · ρrn · ρ−vs,d −vr,d −vs,r Pe,Oc ≤ O c ∩R3+

× dvs,d dvr,d dvs,r ,

(5.29)

where the asymptotic PDF of the random variable vs,d is [1]

−∞ ρ , for vs,d < 0, · pvs,d = ρ−vs,d , for vs,d ≥ 0, ·

where = is defined in (5.27). The PDF of vr,d and vs,r are similar to that of vs,d . Note that the right-hand-side of (5.29) is dominated by the term corresponding to the minimum error exponent, i.e., ·

Pe,Oc ≤ ρ−de (r) , where n [max{2(1−vs,d), 1−(vr,d +vs,r ), 0}−2r]+vs,d +vr,d +vs,r . (vs,d ,vr,d ,vs,r ) 2

de (r) =

inf

∈O c ∩R3+

Similarly, the outage probability PO (R) in (5.28) can be written asymptotically as · PO (R) = ρ−do (r) , where do (r) =

inf

(vs,d ,vr,d ,vs,r )∈O∩R3+

(vs,d + vr,d + vs,r ).

By choosing O = {(vs,d , vr,d , vs,r ) ∈ R3+ | max{2(1 − vs,d ), 1 − (vr,d + vs,r ), 0} ≤ 2r}, the exponent de (r) can be made arbitrarily large by choosing n as large as desired. Thus, the error probability in (5.28) is dominated by the outage probability PO (R) as ·

·

Pe (ρ) ≤ PO (R) + Pe,Oc ≤ PO (R) = ρ−do (r)

(5.30)

where do (r) can be shown to be (1 − r) + (1 − 2r)+ . And it concludes the proof. 

5.2 Single-Relay Fading Channels

211

2

Non−coop NAF LTW−AF

1.8

Diversity Gain d(r)

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

0.2

0.4 0.6 Multiplexing Gain r

0.8

1

Fig. 5.7 DMT comparisons of AF protocols in three-node cooperative channels. (Figure c regenerated and modified from Azarian, El Gamal, and Schniter. 2005 IEEE.)

In Figure 5.7, the DMT of the NAF protocol is compared with the AF protocol proposed by Laneman, Tse, and Wornell in [6] (that is, the LTW-AF protocol described previously) and the non-cooperative case (that is, direct transmission). The NAF protocol shows uniform dominance over the other schemes since it achieves the optimal DMT. Notice that, for multiplexing gains greater than 0.5, the DMT of the NAF scheme coincides with the non-cooperative scheme. This shows that the AF cooperative scheme cannot support multiplexing gain greater than 0.5 due to the half-duplex constraint.

DMT for DF Cooperative Networks We will introduce the dynamic decode and forward (DDF) protocol [1] and show that it achieves the optimal tradeoff for multiplexing gains 0 ≤ r < 0.5. The DDF improves the LTW-DF scheme in [6], which achieve the DMT characterized by d(r) = 2(1 − 2r) (LTW-DF is the same as the basic DF scheme introduced in Section 3.1). Similarly, in the DDF scheme, the source transmits a codeword of length n and rate R. The relay first listens to the source until the mutual information between the source message and the received symbols exceed nR. In this case, it has been shown in [1] that the relay will be able to decode the message with arbitrarily small error probability provided that n is sufficiently large. Suppose that this occurs after n symbols have been received. The relay then decodes and re-encodes the message using an independent Gaussian

212

5 Fundamental Limits of Cooperative and Relay Networks

2

Genie Aided NAF LTW−DF DDF

1.8

Diversity Gain d(r)

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0

0.2

0.4 0.6 Multiplexing Gain r

0.8

1

Fig. 5.8 DMT comparisons of DF protocols in three-node cooperative channels. (Figure c regenerated and modified from Azarian, El Gamal, and Schniter. 2005 IEEE.)

codebook and transmits in the remaining n−n symbol intervals. The method is dynamic in the sense that the duration n for which the relay listens to the source depends on the instantaneous interuser channel condition. Let {Xs [i]}ni=1 and {Xr [i]}ni=n +1 be the signals transmitted by the source and the relay, respectively. The signals received at the destination can be written as

for i = 1, . . . , n hs,d Xs [i] + Wd [i], Yd [i] = (5.31) hs,d Xs [i] + hr,d Xr [i] + Wd [i], for i = n + 1, . . . , n. Since the Gaussian codebook is used, the number of symbol periods that the relay should listen to is A0

@ nR n = min n, . (5.32) log2 (1 + |hs,r |2 P/σr2 ) The achievable DMT with this protocol is given in the following theorem with the proof given in Appendix 5.1. Theorem 5.12. The DMT achieved with a single relay complying with DDF is given by

2(1 − r), if r ∈ [0, 0.5] d(r) = (5.33) (1 − r)/r, if r ∈ (0.5, 1]. In Figure 5.8, we plot the DMT curves for the DDF, the NAF, and the LTW-DF schemes. The DMT of the LTW-DF is 2(1 − 2r). The genie-aided

5.3 Multi-Relay Networks

213

Broadcast Cut

W1 Y1

Relay 1 Encoder

X1

Relay 2 Encoder

X2

W2 U

Encoder

Y2

Xs

WL YL

Wd Yd

Decoder

U

. . . . Relay L XL Encoder

Multiple Access Cut

Fig. 5.9 Gaussian relay channel with multiple relays.

scheme, where the relay is assumed to know a priori the message sent by the source, serves as an upper bound to the DMT of all schemes and is also plotted for comparison. One can see that the DDF scheme achieves the optimal DMT for 0 ≤ r ≤ 12 since it coincides with the DMT of the genieaided scheme. However, for 12 < r ≤ 1, a gap is observed between the curves of the DDF scheme and the genie-aided scheme. The optimal DMT for threenode networks in this region is still unknown. And the DDF performs better than the NAF. It results from the fact that AF forwards a noisy observation from the source while the DDF forwards a clean re-coded message.

5.3 Multi-Relay Networks Consider a large relay network consisting of one source, one destination, and L relay nodes as shown in Figure 5.9. Let u be the message transmitted by the source that is encoded into a length n codeword Xns = [Xs [1], Xs [2], . . . , Xs [n]], whose components are complex and satisfies the power constraint n 2 ) 1  ( E Xs [i] ≤ Ps . n i=1 At time i, relay observes Y [i] = hs, Xs [i] + W [i]

214

5 Fundamental Limits of Cooperative and Relay Networks

where Wn = [W [1], . . . , W [n]] consists of i.i.d. circularly symmetric Gaussian random variables of mean zero and variance σr2 . And the noises over different relays are mutually independent. Based on the sequence of observations {Y [i]}ni=1 , relay generates the codeword Xn = [X [1], . . . , X [n]] in a causal fashion such that X [i] = f,i (Y [i − 1], Y [i − 2], . . . , Y [1]). The codewords satisfy the total power constraint as L n  2 ) 1  ( E X [i] ≤ Pr . n i=1 =1

The output of the relay channel, i.e., the signal received at the destination, is given by L  Yd [i] = hs,d Xs [i] + h,d X [i] + Wd [i] =1

{Wd [i]}ni=1

where are i.i.d. circularly symmetric Gaussian random variables of mean zero and variance σd2 . In the section, the relays are assumed full-duplex. All channels are known perfectly at each node. Although there are small differences compared to the Gaussian relay channel described in Section 5.1 due to the additional channel gains, we still call this channel (with full channel state information at the transmitters) as the Gaussian multi-relay channel. The channels without full channel state information at the transmitters are considered in Section 5.3.3. Notice that the Gaussian multi-relay channel is not a degraded relay channel, thus the capacity is unknown for any finite value of the relay number L. However, meaningful upper and lower bounds can be obtained as shown in [8]. Furthermore, it was also shown in [8] that the upper and lower bounds may coincide for certain cases as L tends to infinity, which yields the asymptotic capacity of the large Gaussian relay channel.

5.3.1 Upper Bound of Gaussian Multi-Relay Networks In Sections 5.3.1 and 5.3.2, we discuss the upper bound and lower bound of Gaussian relay networks with L relays. To focus on the discussion of the relay links, it is assumed that direct link is too weak to be considered. Further results on these bounds that take into account the direct link are developed in [8]. To derive the upper bound, the cut-set bound in Section 5.1.1 is generalized as the following. Corollary 5.1 (Cut-Set Bound [8]). Let Ri,j , for i, j ∈ {s, 1, 2, . . . , L, d}, be the achievable rate between sender i and receiver j. For any partitioning of the network into two sets S, S c ⊂ {s, 1, 2, . . . , L, d} such that S ∩ S c = ∅

5.3 Multi-Relay Networks

215

and S ∪ S c = {s, 1, 2, . . . , L, d}, the following inequality must be satisfied  Ri,j ≤ max I(XS ; YS c |XS c ), pXS ,XS c

i∈S,j∈S c

where pXS ,XS c is the joint distribution of XS , XS c which satisfies the power constraints. Proof. Here we provide a simple “genie-aided arguments” to invoke the wellknown MIMO capacity result. Suppose that all the nodes in S can cooperate arbitrarily, and also the nodes in S c . The rate upper bound of this new cooperative system is an upper bound of the original one since the original one is merely one way to implement arbitrary cooperations. And the new cooperative system is simply a point-to-point MIMO channel whose rate upper bound is given by max I(XS ; YS c |XS c ). pXS ,XS c

And this concludes the proof.  Two natural cuts can be considered as shown in Figure 5.9: one is the “broadcast cut”, where we take S = {s} and S c = {1, . . . , L, d}, and the other is the “multiple access cut”, where we take S = {s, 1, . . . , L} and S c = {d}. In the broadcast cut, the mutual information to be maximized is given by I(Xs ; Yd , Y1 , . . . , YL |X1 , . . . , XL ) = I(Xs ; Y˜d , Y1 , . . . , YL ) where Y˜d = Yd −

L 

h,d X .

=1

In this case, the observations Y˜d , Y1 , . . . , YL can be viewed as the output of a single-input multiple-output (SIMO) channel and the capacity is given by [9] [10]   L PS  2 ˜ CBC = max I(Xs ; Yd , Y1 , . . . , YL ) = log2 1 + 2 (5.34) |hs, | . pXs σr =1

where the maximum is over all input distribution to satisfy power constraints. And CBC serves as the broadcast cut-set bound. On the other hand, in the multiple access cut, the upper bound CMAC is obtained by maximizing the mutual information I(Xs , X1 , . . . , XL ; Y ) over all pXs ,X1 ,...,XL subject to the power constraints

216

5 Fundamental Limits of Cooperative and Relay Networks

2 ) 1  ( E Xs [i] ≤ Ps n i=1 n

and

L n  2 ) 1  ( E X [i] ≤ Pr . n i=1 =1

To simplify our problem, we relax the problem by considering instead the joint power constraint n L n 2 )  2 ) 1  ( 1  ( E Xs [i] + E X [i] ≤ Ps + Pr . n i=1 n i=1 =1

The problem then reduces to a multiple-input single-output (MISO) channel with capacity CMAC , where the multiple-access cut-set bound CMAC satisfies   L Ps + Pr  2 (5.35) |h,d | . CMAC = log2 1 + σd2 =1

Proposition 5.1 (Upper Bound). The capacity of a full-duplex Gaussian multi-relay channel as illustrated in Figure 5.9 is upper-bounded by Cf ≤ min(CBC , CMAC ),

(5.36)

where the broadcast cut-set bound CBC is given in (5.34) and the multipleaccess cut-set bound CMAC is given in (5.35).

5.3.2 Lower bound of Gaussian Multi-Relay Networks and Asymptotic Capacity Results To obtain a lower bound on the capacity, let us consider a particular case where the relays simply amplify and forward the signals received from the source to the destination, while satisfying the power constraints Ps and Pr . In this case, the channel between the source and the destination can be viewed as a point-to-point channel. Clearly, Cp < Cf , where Cp is the capacity of this point-to-point channel. Moreover, it is well-known that in the point-to-point channel, the separation theorem [3] holds, that is, R(Dachieve ) < Cp , where Dachieve is the minimum achievable distortion and R(·) is the ratedistortion function of the source with respect to the selected distortion measure. In this case, R(·) is the rate-distortion function of Gaussian source with mean-square error (MSE) distortion measure [3] and log2

Ps < Cp < Cf . Dachieve

(5.37)

5.3 Multi-Relay Networks

217

Now the goal is to find Dachieve . Suppose that the source transmits an uncoding i.i.d. Gaussian sequence {Xs [i]} with variance Ps . The relay delays the received signal by one time unit in order to satisfy the causality constraint and scale it to their desired power level. The signal received at relay at time i is Y [i] = hs, Xs [i] + W [i] and the output at time i + 1 is ' X [i + 1] = e

jθ

P 2 |hs, | Ps

+ σr2

Y [i]

where P is the power emitted by relay and θ is the appropriately chosen phase rotation. In this section, we assume that the destination is not able to received signal directly transmitted by the source for simplicity. The signal received at the destination is Yd [i + 1] =

L 

h,d X [i + 1] + Wd [i + 1]

(5.38)

=1

=

L 

a (hs, Xs [i] + W [i]) + Wd [i + 1],

=1

'

where a = h,d e

jθ

P 2 |hs, | Ps

+ σr2

.

Suppose that a linear scale of the received signal, i.e., γYd [i + 1], is used to estimate the message Xs [i]. The mean square error can be written as  E |Xs [i] − γYd [i + 1]|2   2   L L      a hs, − γ a W [i] − γWd [i + 1] = E Xs [i] 1 − γ =1

=1

 2   L L       2 2 2 2 = Ps 1 − γ a hs,  + |γ| σd + σr |a | .   =1

=1

By taking the derivative of the above with respect to γ and setting it to zero, the optimal value of γ can be obtained as  ∗ L Ps h a s,  =1 γopt = .  2 L  L  2 2 2 Ps  =1 hs, a  + σd + σr =1 |a | The minimum achievable mean square error distortion is then given by

218

5 Fundamental Limits of Cooperative and Relay Networks

 E |X[i] − γopt Y [i + 1]|2   L Ps σd2 + σr2 =1 |a |2 = L L Ps | =1 hs, a |2 + σd2 + σr2 =1 |a |2 Ps σr2

=

|hT a|2

a 2 +σd2 /σr2

Ps + σr2

where vectors h = [hs,1 , . . . , hs, ]T and a = [a1 , a2 , . . . , aL ]T are defined to simplify notations. Now the goal is to choose a to further minimize the distortion. Following [5], by taking ' Pr ∗ a = h , B(L) s, where B(L) =

L =1

|hs, |2 (|hs, |2 Ps +σr2 ) , |h,d |2

Dachieve =  L

we can achieve the distortion

( Ps σr2 +

B(L)σd2  2 Pr L =1 |hs, |

2 2 =1 |hs, | Ps + σr +

)

B(L)σd2  2 Pr L =1 |hs, |

.

Now we have the minimum distortion Dachieve . Then from (5.36) and (5.37), we know that R(Dachieve ) ≤ Cf ≤ min{CBC , CMAC }. With considering direct link, two capacity bounds can be obtained with similar derivation, and further results can be found in [8] for readers’ reference. In Fig. 5.10, we show CBC and R(Dachieve in terms of the number of relay nodes numerically. The value of CBC can be regarded as an upper bound of Cf , and one can observe the asymptotic difference between two bounds. In the example, the source and the destination are located at (-0.25, 0) and (0.25, 0), respectively, in a Cartesian coordinate system. Two capacity bounds in Fig. 5.10 is contributed by the relay links as well as direct link [8]. The relay nodes are uniformly distributed in a disk of unit area centered at the origin, except a dead zone which is a circle of radius 0.01 centered at the source. The channel coefficient between nodes i and j is given by 1/dα i,j where di,j is the distance between the two nodes and α is the path loss exponent. The source transmission power is Ps = 10 and the relays follow total power constraint Pr = 10L. The noise variances are set as one. The bounds obtained from a single realization (dashed lines) and the average over 100 realization (solid lines) are both illustrated. The curves indicates the log2 L behavior of capacity and the difference between the upper and lower bounds converges as L goes to infinity [8]. The convergence of the asymptotic difference is also found in the following theorem. Theorem 5.13. Given regularity conditions on the asymptotic properties of the relay power and channel parameters, it holds that

5.3 Multi-Relay Networks

219

Fig. 5.10 Numerical evaluation of upper and lower bounds of capacity versus number of c relay nodes. (From Gastpar and Vetterli with modified labels. 2005 IEEE.)

lim (CBC − R(Dachieve )) = ΔBC ,

L→∞

and lim (CMAC − R(Dachieve )) = ΔMAC ,

L→∞

where ΔBC , ΔMAC are constants that depend on the transmit power and channel parameters. The detail regularity conditions can be found in [5]. By showing that the asymptotic difference between the upper and lower bound converges to a constant, we can then obtain the asymptotic scaling behavior of Cf . The theorem implies that the capacity of large Gaussian relay channels scales with the minimum between the capacity of the broadcast and multiple-access cuts. The latter has closed form expressions as given in (5.36). Moreover, it also shows that the amplify-and-forward (AF) relay scheme is asymptotically optimal in the sense that it achieves the same scaling performances as the network capacity as L goes to infinity.

5.3.3 Multi-Relay Fading Channels Now we consider channels without full channel state information at the transmitters while the receivers have the full channel state information. As in Sec-

220

5 Fundamental Limits of Cooperative and Relay Networks

tion 5.2, the fast and slow fading channels are considered. For the fast fading channels, we consider the phase fading model as in Section 5.2.1, that is, for each relay , ejθ,d [i] h,d [i] =  , dα ,d where ejθ,d [i] is i.i.d. in the time domain and uniformly distributed in [0, 2π), d,d is the distance between the relay to destination, and α is the attenuation exponent. The channels hs,d and hs, , 1 ≤ ≤ L are defined similarly. The random phase of each receiver is independent of each other and unknown to the corresponding transmitter. The capacity result of Theorem 5.8 in Section 5.2.1 can be extended as the following [5]. Theorem 5.14. If the relays are close to the source, the ergodic capacity for full-duplex phase fading relay channel with L relays is   L  Pr Ps Cf = max log2 1 + α 2 + , 2 Pr1 ,...,PrL ds,d σd dα l,d σd =1

where the maximization is over all power L allocation strategies over the relays satisfying the total power constraint =1 Pr < Pr . The formal mathematical descriptions on the distance constraint between relays and the source to validate this theorem can be found in [5, Theorem 7]. Again, DF achieves the capacity described in this theorem. Now we turn to the slow fading channels as in Section 5.2.2 and study the DMT for half-duplex channels. The channel model is a simple extension of the one in 5.2.2 with L relays and omitted here. First, the genie-aided diversity upper bound in Theorem 5.9 can be extended to multi-relay systems with L relays as the following theorem. Theorem 5.15. The diversity gain of the L-relay cooperative network is upper-bounded by d(r) ≤ (L + 1)(1 − r), where the multiplexing gain 1 ≥ r > 0. The NAF protocol in Theorem 5.11 can be easily extended to multiple relay cases (i.e., L ≥ 1). In these cases, each transmission is divided into L cooperative frames where the L relays take turns in relaying the codewords transmitted by the source. More specifically, as shown in Figure 5.11, relay

overhears the codeword transmitted by the source in the first half of the

-th cooperative frame while the relay amplifies and forwards the codeword in the second half of the frame. The achievable DMT was given in [1] and summarized in the following theorem. Theorem 5.16. The achievable diversity gain of the NAF protocol with L relays is characterized by

5.3 Multi-Relay Networks

221

Fig. 5.11 Nonorthogonal amplify-and-forward protocol with L relays. (Figure regenerated c and modified from Azarian, El Gamal, and Schniter. 2005 IEEE.)

d(r) = (1 − r) + L(1 − 2r)+ . The DDF scheme in Theorem 5.12 can also be extended to the case of multiple relays. Again, suppose that there are L relays in the network. Similar to the single relay scenario, all relays first listen to the source at the beginning of each transmission block until the mutual information between the source message and the locally received signal exceeds nR, that is, they can successfully decode the message, and forward a new codeword. It is worthwhile to notice that the decoding time at each relay may be different. The DMT achieved in this case has also been derived in [1] and is summarized in the following. Theorem 5.17. The diversity gain achieved by the DDF scheme with L relays is characterized by ⎧ 1 ⎨ (L + 1)(1 − r), for 0 ≤ r ≤ L+1 , L(1−2r) 1 d(r) = 1 + 1−r , for L+1 < r ≤ 0.5, ⎩ 1−r for 0.5 < r ≤ 1. r , From the above theorem, DDF is optimal when multiplexing gain r is low as 1/(L + 1) ≥ r > 0, since DDF achieves the genie-aided upper bound in Theorem 5.15. In Figure 5.12, we demonstrate the DMT curves for NAF, DDF, and genie-aided protocols with the number of relays being 4. It is observed that the diversity order of DDF drops rapidly as the multiplexing gain increases. Nevertheless, DDF is optimal with small multiplexing gain and it outperforms NAF for any multiplexing gain r ∈ (0, 1).

222

5 Fundamental Limits of Cooperative and Relay Networks

5

Genie Aided NAF DDF

4.5 Diversity Gain d(r)

4 3.5 3 2.5 2 1.5 1 0.5 0 0

0.2

0.4 0.6 Multiplexing Gain r

0.8

1

Fig. 5.12 Diversity-multiplexing tradeoff for NAF, DDF, and genie-aided protocols with L = 4 relays. (Figure regenerated and modified from Azarian, El Gamal, and Schniter. c 2005 IEEE.)

Appendix 5.1 Proof of Theorem 5.12 The basic proof steps follow those in the proof of Theorem 5.11. We first derive the error probability formula corresponding to (5.28). For a given channel realization, the conditional error probability of the ML decoder can be written as Pe|h = Pe,Erc |h + Pe,Er |h ≤ Pe|Erc ,h + PEr |h where h = [hs,d , hr,d , hs,r ]T and Er is the event that a decoding error occurs at the relay. As described previously in (5.32), the probability of error at the relay can be made arbitrarily small when the mutual information between the source message and the received signal exceeds nR, provided that n is sufficiently large. Thus, the error probability is asymptotically bounded as Pe|h ≤ Pe|Erc ,h . By taking the average over channel realizations, the average error probability is given by ·

Pe ≤ Pe|Erc ≤ PO|Erc + Pe,Oc |Erc .

(5.39)

We neglect the lower-script Erc in the following texts. According to [1], suppose that the rate is R = r log2 ρ and the codeword length is n, the second term in (5.39) can be bounded as

5.3 Multi-Relay Networks

223

2

Fig. 5.13 Outage region for the DDF protocol with a single relay (f ≤ 0.5). (Figure c regenerated and modified from Azarian, El Gamal, and Schniter. 2005 IEEE.)

·

Pe,Oc ≤ ρ−de (r) , where      n n (1 − vs,d )+ + 1 − (1 − min{vs,d , vr,d })+ − r n inf de (r) = n n v∈O c ∩R3+ (5.40)

 + (vs,d + vr,d + vs,r ) , and v = [vs,d , vr,d , vs,r ]. Therefore, since ·

PO = ρ−do (r) where do (r) =

inf

v∈O∩R3+

(vs,d + vr,d + vs,r ),

(5.41)

we may choose the outage event as   

 0  n 3+  n + + (1 − min{vs,d , vr,d }) ≤ r (5.42) O = v ∈ R  (1 − vs,d ) + 1 − n n by observing from (5.40) and (5.41), so that the error probability in (5.39) is ·

dominated by the outage probability PO , i.e., Pe ≤ PO . In this case, we need only to determine the value of do (r). Let us consider four different cases: (i) vs,d , vr,d ≥ 1; (ii) 0 ≤ vs,d ≤ 1, vr,d ≥ 1; (iii) vs,d ≥ 1, 0 ≤ vr,d ≤ 1; (iv) 0 ≤ vs,d , vr,d ≤ 1. Please note that, by (5.32), it follows that

224

5 Fundamental Limits of Cooperative and Relay Networks

2

Fig. 5.14 Outage region for the DDF protocol with a single relay (f > 0.5). (Figure c regenerated and modified from Azarian, El Gamal, and Schniter. 2005 IEEE.)

f

0

0

r n · r log2 ρ = min 1, . = min 1, n log2 (|hs,r |2 ρ) 1 − vs,r

(5.43)

The first three cases all exceed the diversity order achieved by the genie-aided scheme, i.e., 2(1 − r) [1], and thereby cannot be the dominating factors in the DMT of the DDF scheme. Case (iv) is then of particular interest. In case (iv) where 0 ≤ vs,d , vr,d ≤ 1, the inequality in (5.42) is * + f (1 − vs,d ) + 1 − f (1 − vs,d ) = 1 − vs,d ≤ r ⇒ vs,d ≥ 1 − r, for vs,d ≤ vr,d , and is * + * + f (1 − vs,d ) + 1 − f (1 − vr,d ) ≤ r ⇒ f vs,d + 1 − f vr,d ≥ 1 − r, for vs,d ≥ vr,d . The possible values of (vs,d , vr,d ) are plotted in Figure 5.13 for f ≤ 12 and in Figure 5.14 for f > 12 . Moreover, for vs,r , it also holds by (5.43) that f = 1−vr s,r ≥ r and that vs,r = 1 − fr . Let us consider the two cases regarding the value of f . For r ≤ f ≤ 12 , as can be observed from Figure 5.13, inf (vs,d ,vr,d ,0)∈O

vs,d + vr,d

takes on the value 2(1 − r). This is equivalent to the DMT of the genieaided scheme. Thus we can always choose f = r such that vs,r = 0 and do (r) = 2(1 − r). On the other hand, for f > max{r, 12 }, the minimum value r of vs,d + vr,d is given by 1−r f . With vs,r = 1 − f , the diversity order is

References

225

do (r) =

inf f >max{r,0.5}

1+

1 − 2r . f

Then it can be checked that the results in Theorem (5.12) is valid as in [1]. 

References 1. Azarian, K., El Gamal, H., Schniter, P.: On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels. IEEE Transactions on Information Theory 51(12), 4152–4172 (2005) 2. Cover, T., El Gamal, A.: Capacity theorems for the relay channel. IEEE Transactions on Information Theory 25(5), 572–584 (1979) 3. Cover, T.M., Thomas, J.A.: Elements of Information Theory, 2nd edn. WileyInterscience (2006) 4. Host-Madsen, A., Zhang, J.: Capacity bounds and power allocation for wireless relay channels. IEEE Transactions on Information Theory 51(6), 2020–2040 (2005) 5. Kramer, G., Gastpar, M., Gupta, P.: Cooperative strategies and capacity theorems for relay networks. IEEE Transactions on Information Theory 51(9), 3037–3063 (2005) 6. Laneman, J., Tse, D., Wornell, G.: Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Transactions on Information Theory 50(12), 3062–3080 (2004) 7. van der Meulen, E.C.: Three-terminal communication channels. Advances in Applied Probability 3, 120–154 (1971) 8. Gastpar, M., Vetterli, M.: On the capacity of large Gaussian relay networks. IEEE Transactions on Information Theory 51(3), 765–779 (2005) ˙ 9. Telatar, I.E.: Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications 10(6), 585–595 (1999) 10. Tse, D., Viswanath, P.: Fundamentals of Wireless Communication. Cambridge University Press (2005) 11. Wyner, A., Ziv, J.: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 22(1), 1–10 (1976) 12. Zheng, L., Tse, D.N.C.: Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory 49(5), 1073– 1096 (2003)

Chapter 6

Cooperative Communications with Multiple Sources

Previously, we focused on cooperative systems where only one user is allowed to act as the source at any instant in time while the other users serve as relays of the source. However, in multiuser systems, multiple sources may be accessing the cooperative channel simultaneously and, thus, multiple access strategies must be devised to separate their signals in either time, frequency, code, or space. In this chapter, we will examine different multiple access schemes for cooperative communication systems, including time-division multiple access (TDMA), frequency-division multiple access (FDMA), code-division multiple access (CDMA), and space-division multiple access (SDMA) schemes. In TDMA/FDMA systems, sources transmit over orthogonal time or frequency channels, where radio resources must be properly allocated to fully exploit the advantages of cooperation. In CDMA or SDMA systems, users transmit simultaneously over different code or spatial dimensions, but may experience multiple access interference (MAI) due to practical difficulties in achieving orthogonality among the different dimensions. Therefore, precoding techniques at the relays or multiuser detection schemes at the destination must be employed to mitigate the MAI. In addition to the multiple access issues, we will also introduce different partner selection policies and show how they can be used to further enhance the cooperative advantages in multiuser systems. Note that this chapter is devoted to the studies on multiple access techniques, which are used to multiplex transmissions from different users. Issues involving user competition and collision resolution will be discussed in Chapter 8, where medium access control (MAC) policies are introduced.

6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA) In multiuser cooperative systems, one may encounter different network topologies depending on the role of the relays and the number of destinaY.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_6, © Springer Science+Business Media, LLC 2010

227

228

6 Cooperative Communications with Multiple Sources

(a) Designated Relays

(b) Shared Relays Fig. 6.1 Example of multi-source cooperative networks with different relay functionalities.

tions. Specifically, we can consider the case of designated relays, where each source is served exclusively by one or multiple relays, as shown in Fig. 6.1(a), and the case of shared relays, where multiple sources are served by a common set of relays, as shown in Fig. 6.1(b). In the case of designated relays, the resources of a given relay are allocated completely to a single source and, thus, the system is simpler to implement. However, the diversity gain achievable in this case is often limited since the number of relays that can be designated to one particular source is usually small. In the case of shared relays, more than one source may be transmitting through a common set of relays and, thus, the resources at the relays must be properly allocated to maximize cooperative advantages received by the sources. Higher diversity gains can potentially be

6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA)

229

achieved compared to the case of designated relays. Furthermore, we can also consider different settings based on the number of destinations. Two scenarios will be considered in this chapter, i.e., the ad hoc scenario, where each source transmits to a different destination, and the uplink scenario, where the sources transmit to a common destination (or base station). In TDMA or FDMA systems, each transmission entity is assigned an orthogonal time or frequency channel to conduct its transmission. In systems with designated relays, each source and its relays can form a transmission entity that is treated as a super-user and allocated a single orthogonal channel for transmission. The channel can be further divided into subchannels if the cooperation requires multi-phase transmissions. The rate achievable as a group, through cooperation, can then be viewed as the rate achievable by the transmission entity and, thus, the problem becomes identical to that of non-cooperative systems. Related studies on the use of TDMA/FDMA for systems with designated relays can be found in [2]. The problem is considerably more interesting when shared relays are employed. In this case, sources may compete for resources at the relays and, thus, efficient resource allocation policies are essential to achieve high cooperative gains. These issues will be discussed for two TDMA scheduling schemes: round-robin scheduling, where the sources take turns in accessing the relays, and opportunistic scheduling, where the relays dynamically serve the source with the best effective channel in each time slot. The discussions are focused on TDMA systems while extensions to FDMA systems are left as exercises for the readers.

6.1.1 Round-Robin Scheduling Round-robin scheduling is one of the simplest TDMA policies, where time is divided into equal-length time slots and each source takes turns in accessing the common set of relays. To simplify our discussions, we consider in this section the case where only one relay exists in the network and where the basic cooperative schemes introduced in Chapter 3 are employed. The case of multiple relays can also be examined similarly by considering the cooperative strategies introduced in Chapter 4. Moreover, it is worthwhile to note that, in round-robin scheduling, the users are scheduled according to the order of their indices (not the quality of their channels). Hence, the sources that are scheduled to transmit often will not be able to support high data rates and, thus, will affect the overall system throughput. This effect can be mitigated with proper resource allocation across time. Let us consider the ad hoc scenario shown in Fig. 6.2 where the sources are transmitting to different destinations through a common relay. Suppose that there are K distinct source-destination pairs, denoted by {(sk , dk )}K k=1 , and that each source-destination pair is allocated 1/K share of time to transmit.

230

6 Cooperative Communications with Multiple Sources

Fig. 6.2 A cooperative TDMA system with a single relay serving multiple sourcedestination pairs.

Let hk,r and fk,k be the channel coefficients between source sk and relay r and between source sk and destination dk , respectively, and let gr,k be the channel coefficient between relay r and destination dk . By considering the basic DF relaying scheme, the capacity of the k-th source-destination pair under the assistance of the common relay is given by   , (k) Psk |fk,k |2 1 Pr |gr,k |2 (DF) Ck log2 1 + , = min + 2K σd2 σd2  0 Ps |hk,r |2 1 log2 1 + k 2 , (6.1) 2K σr (k)

where Psk is the transmission power of source sk , Pr is the transmission power used by the relay when forwarding sk ’s messages, and σd2 and σr2 are the noise variances at the destinations and the relay, respectively. The additional 1/K factor comes from the fact that time is divided equally among K users. By defining ϕk,k  |fk,k |2 /σd2 , γr,k  |gr,k |2 /σd2 , and ηk,r  |hk,r |2 /σr2 as the SNRs on the sk -dk , r-dk and sk -r links, the capacity expression in (6.1) can be written as 4   5 1 (DF) Ck min log2 1 + Psk ϕk,k + Pr(k) γr,k , log2 (1 + Psk ηk,r ) . = 2K (6.2) Due to channel variations among different users, the relay can dynamically adjust the powers used to forward the sources’ data in order to maximize the aggregate throughput of the K sources [21]. That is, given the instantaneous (k) CSI, the powers {Pr }K k=1 are chosen to maximize the sum rate of all sourceK (k) destination pairs subject to a sum power constraint k=1 Pr ≤ Pr . The optimal power allocation problem can be formulated as follows:

6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA) K 

max (k) K }k=1

{Pr

231

(DF)

Ck

(6.3)

Pr(k) ≤ Pr , Pr(k) ≥ 0, k = 1, 2, ..., K.

(6.4)

k=1

subject to

K  k=1

By neglecting the second term inside the minimum in (6.2), the problem reduces to K 

max (k) K }k=1

{Pr

  log 1 + Psk ϕk,k + Pr(k) γr,k

(6.5)

Pr(k) ≤ Pr , Pr(k) ≥ 0, k = 1, 2, ..., K.

(6.6)

k=1

subject to

K  k=1

The Lagrange function can be written as L({Pr(k) }K k=1 )

=

K 

 log 1 + Psk ϕk,k +

Pr(k) γr,k



 + μDF Pr −

k=1

K 

 Pr(k)

.

k=1

Then, by Karush-Kuhn-Tucker (KKT) conditions, the optimal power allocation can be computed as  Pr(k) =

1 μDF

−

1 + Psk ϕk,k γr,k

+ , ∀k,

(6.7)

where (x)+ = max(x, 0) and μDF is a constant chosen to satisfy the total K (k) power constraint k=1 Pr ≤ Pr . This is in the form of a water-filling solution, where power is distributed among the various bins corresponding to different users in a way that is analogous to the distribution of water when poured into a vessel (see Chapter 2.3). However, when taking into consideration the second term inside the minimization of (6.2), the capacity of each source-destination pair will be upper 1 bounded by the capacity of the sk -r link (i.e., 2K log2 (1 + Psk ηk,r )) regardless of the power allocated to the k-th source-destination pair. Hence, the (k) value of Pr should not exceed Psk (ηk,r − ϕk,k )/γr,k ; otherwise, extra power will be expended with no increase in the system sum rate. The optimal power allocation leads to a cave-filling solution with ceiling layer given by (1 + Psk ηk,r )/γr,k and base layer given by (1 + Psk ϕk,k )/γr,k , as illustrated in Fig. 6.3 for the case of 5 users. The water poured into the cave corresponding to each source-destination pair will be limited by its ceiling layer and will flow into the cave corresponding to other pairs. The distance between the water-level (or the ceiling layer) and the base layer is the allocated power (k) Pr . Notice that the base layer in Fig. 6.3 is determined by the quality of

232

6 Cooperative Communications with Multiple Sources

Hj- 5 ( Hj+

Hj, ) 

Hj* Hj)

k*

k)

k+

k,

k-

Fig. 6.3 Illustration of the cave-filling solution for a TDMA cooperative network with 5 source-destination pairs.

the sk -dk link while the ceiling layer is determined by the quality of the sk -r link. The optimal solution is given by Pr(k) =

0 + 

1 + Psk ϕk,k 1 1 + Psk ηk,r − min , , ∀k, μDF γr,k γr,k

(6.8)

K (k) where 1/μDF is chosen to satisfy the total power constraint k=1 Pr ≤ Pr . More specifically, for users whose ceiling layers are lower than the waterlevel 1/μDF , e.g., users 1 and 2 in Fig. 6.3, the allocated relay power is given by Ps (ηk,r − ϕk,k ) Prk = k , (6.9) γr,k and the resulting capacity is given by (DF)

Ck

=

1 log2 (1 + Psk ηk,r ) , 2K

(6.10)

which means that the DF relay capacity is bounded by that of the sk -r link. For users whose ceiling layers are higher than 1/μDF , e.g., user 3 and user 4 in Fig. 6.3, the power is given by   1 + Psk ϕk,k 1 Prk = (6.11) − μDF γr,k and the corresponding capacity of the k-th source-destination pair is

6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA) (DF)

Ck

=

1 log2 2K



γr,k μDF

233

 .

(6.12)

However, if the user’s base layer is higher than 1/μDF (e.g., user 5 in Fig. 6.3) or even the ceiling layer (i.e., ϕk,k > ηk,r ), no power will be allocated at the relay to forward its data (i.e., Prk = 0). The round-robin based TDMA system can also be performed using the basic AF relaying technique. Utilizing the capacity expressions given in Chapter 3, we can also find the optimal power allocation by maximizing the system sum rate. Specifically, when the basic AF relaying scheme is employed, the capacity of the k-th source-destination pair, i.e., (sk , dk ), can be expressed as   (k) 1 Psk Pr ηk,r γr,k (AF) log2 1 + Psk ϕk,k + . (6.13) = Ck (k) 2K 1 + Psk ηk,r + Pr γr,k The optimal power allocation can be found by maximizing the sum rate of all sources as given below [21]: K 

max (k) K }k=1

{Pr

(AF )

Ck

(6.14)

Pr(k) ≤ Pr , Pr(k) ≥ 0, ∀k.

(6.15)

k=1

subject to

K  k=1

The Lagrange function can be written as  K  (k) K log 1 + Psk ϕk,k + L({Pr }k=1 ) = k=1

(k)

Psk Pr ηk,r γr,k



(k)

1 + Psk ηk,r + Pr γr,k   K  (k) . Pr + μAF Pr − k=1

By KKT conditions, the optimal power allocation is then given by 

⎛ ak ⎜ −( bk

Pr(k) = ⎜ ⎝

+ 2) +

ak bk

2 +

4ak μAF

(1 +

2(ak + bk )

where Psk ηk,r /(Psk ηk,r + 1) , (1 + Psk ϕk,k )/γr,k γr,k , bk = Psk ηk,r + 1

ak =

⎞+ ak bk ) ⎟

⎟ , ⎠

(6.16)

234

6 Cooperative Communications with Multiple Sources

and μAF is a constant chosen to satisfy the sum power constraint in (6.15). The power allocation problem described above can also be studied with the more advanced relaying strategies introduced in Chapter 3 and for the multirelay cooperative strategies introduced in Chapter 4. However, these problems are left as exercises for interested readers. It is worthwhile to remark that, in multi-relay systems, individual power constraints at the relays may render the problem more difficult and is an interesting topic for future investigation. The round-robin based TDMA system introduced in this subsection can be easily extended to FDMA systems, where users’ transmissions are multiplexed in frequency rather than in time. More specifically, in FDMA systems, the source-destination pairs are allocated orthogonal frequency bands with equal bandwidth and each frequency band is further divided into two sub-bands, used for Phase I and Phase II transmissions performed by the source and the relay, respectively. However, to maintain causality, the data forwarded by the relay in the second sub-band must be received in the previous time slot (in the first sub-band). In this case, an additional time slot is needed for the source to initiate the transmission at the very beginning, but the average rate will not be affected as long as the transmission proceeds over a sufficiently large number of time slots. In frequency-selective environments, the power allocated to each source-destination pair can also be obtained similar to (6.8) or (6.16). Although the round-robin scheduler is easy to implement in practice, it does not fully exploit the spatial diversity existing in multiuser communication systems. In fact, instead of allocating less power to a source when it does not have a favorable channel, it is more beneficial to simply schedule the user with a better channel to transmit. This leads to the opportunistic scheduling scheme introduced in the next subsection.

6.1.2 Opportunistic Scheduling Instead of scheduling users in a predetermined order (as in round-robin scheduling), the opportunistic scheduling scheme dynamically selects the best user (e.g., the user that yields the highest effective SNR at its destination) to transmit in each time slot, exploiting the so called multiuser diversity. Let us consider a cooperative TDMA system that consists of K sources transmitting to their respective destinations over L shared relays. Assume that the channel remains constant over each cooperative transmission but varies independently from block to block. If the k-th source is served in the mth time slot, the signals received at the L relays in Phase I can be represented ( )T (k) (k) (k) (k) by the vector yr [m] = y1 [m], y2 [m], · · · , yL [m] , where yr(k) [m] =

-

Psk hk,r xk [m] + wr [m].

(6.17)

6.1 Time/Frequency-Division Multiple Access (TDMA/FDMA)

235

Fig. 6.4 Illustration of opportunistic scheduling with two source-destination pairs.

and hk,r = [hk,1 , hk,2 , · · · , hk,L ]T is the vector of channel coefficients between source sk and the L relays, xk [m] is source symbol with unit energy, i.e., E[|xk [m]|2 ] = 1, ∀k, and wr [m] = [wr1 [m], . . . , wrL [m]]T ∼ CN (0, σr2 I) consists of the noise at the relays. In Phase II of the same time slot, the relays will together forward the source’s data to its respective destination. Suppose that an AF multi-relay (k) policy is employed such that the amplifying factor α is imposed at relay r when forwarding the data of source k. Let g,k be the channel coefficient between relay r and destination dk . Then, the signal received at destination dk can be expressed as ydk [m] =

L 

(k) (k)

g,k α y [m] + wd [m]

=1

=

T T Psk gr,k A(k) hk,r xk [m] + gr,k A(k) wr [m] + wd [m]

where gr,k = [g1,k , g2,k , · · · , gL,k ]T is the channel vector between the relays (k) (k) (k) and destination dk , A(k) = diag(α1 , α2 , · · · , αk ) is a diagonal matrix consisting of the amplifying coefficients of source sk at the L relays, and wd [m] ∼ CN (0, σd2 ) is the AWGN at the destination. The instantaneous SNR achieved by the k-th source-destination pair in the m-th time slot is given by γk [m] =

T A(k) hk,r |2 Psk |gr,k . * + T A(k) A(k) H g∗ σd2 + σr2 gr,d r,k

(6.18)

To maximize the system sum rate, the source-destination pair with the highest instantaneous SNR will be scheduled to transmit in each time slot. An example with two users is illustrated in Fig. 6.4. We can see that, by select-

236

6 Cooperative Communications with Multiple Sources

M source/destination pairs with L relays at P/σ

20dB

1%−outage aggregate throughput [bps/Hz] RXWDJHDJJUHJDWHWKURXJKSXWELWVVHF+] 









0



0 0 0 0 0 0



















L relays 1XPEHURIUHOD\V







      

Fig. 6.5 Aggregate 1% outage throughput in terms of the number of relays L in adaptive TDMA cooperative systems (From Hammerstrom, Kuhn and Wittneben with modified c labels; [2004] IEEE ).

ing the user with the best SNR, the effective SNR observed by the system remains at a high level. In Fig. 6.5 (obtained from [11]), the aggregate 1% outage throughput is shown with respect to the number of relays L for a cooperative network with K = 1 ∼ 6 source-destination pairs. In this experiment, the amplifying gain at relay r is given by ' Pr (k) ejφ , for k = 1, . . . , K, (6.19) α = Psk |hk, |2 + σr2 where φ is the random phase offset at relay r that is assumed to be uniformly 2 distributed between 0 and 2π. It is also assumed that σr2 = σd2 = σw and 2 that the transmit SNRs of each source and the relays are set as Psk /σw = 2 L × Pr /σw = 20 dB for all k. We can observe that the aggregate throughput increases with the number of source-destination pairs because of the increased multiuser diversity. Moreover, we can see that the increase in the aggregate throughput also increases with the number of relays but saturates rapidly since the multiuser diversity exploited in this case already achieves significant gains to the throughput and, thus, the additional cooperative diversity gains obtained by increasing the number of relays become limited.

6.2 Code-Division Multiple Access (CDMA)

237

The opportunistic TDMA scheduling introduced in this section allows the system to exploit multiuser diversity and achieve a significant increase in the aggregate system throughput. However, this method does not ensure fairness and, thus, users with low average SNR will have little opportunity to transmit. A simple approach to achieving fairness is to limit the maximum number of time slots that can be allocated to each source-destination pair over a given period of time. More sophisticated issues can also be developed based on different fairness criteria, e.g., max-min or proportional fairness etc [16].

6.2 Code-Division Multiple Access (CDMA) Code-division multiple access (CDMA) [1, 8, 26] refers to channel access schemes where sources share the same time and frequency channel but separate their signals by modulating them on different signature codes. If the codes are orthogonal to each other, the receiver will be able to perfectly separate the signals by passing the aggregate signal from multiple users through matched filters that are associated with the codes of different users. Since the code is unique to each user and the resulting waveform occupies a much wider bandwidth than the original signal, the CDMA codes are often referred to as signature or spreading codes (or waveforms). These schemes are thus considered as a subclass of the more general spread spectrum technology [23]. In the past two decades, CDMA technology has drawn much attention from both academia and industry due to its ability to resolve multipath signals, to enable easier frequency planning, and to increase user capacity. It is also the key enabling technology in many modern wireless communication systems such as cdmaOne and cdma2000. In cooperative networks, CDMA systems can be incorporated in a way similar to that of TDMA and FDMA systems if the signature waveforms are perfectly orthogonal to each other. Unfortunately, this is difficult to guarantee in practice due to the lack of perfect synchronization at the receiver, non-ideal waveform design, or overloaded systems etc. As a result, both the relays and the destinations will be subject to multiple access interference (MAI) and significant performance degradations will be observed, especially when conventional single-user detectors are used at the receivers. This loss in performance cannot be reduced by simply increasing the transmission power since the interference increases proportionally with the transmission power of the users. Hence, an error floor will exist as SNR increases. These effects can be even more detrimental if the interfering users are closer to the destination than the source, resulting in the so called near-far effect. To mitigate the MAI, multiuser detection (MUD) schemes, where the signals of multiple users are detected jointly at the receiver, have been investigated extensively in the literature for conventional non-cooperative systems [9, 17, 18, 25]. While the use of MUD have been avoided in the past due

238

6 Cooperative Communications with Multiple Sources

Fig. 6.6 Illustration of a CDMA uplink system with designated relays.

to its high complexity, it is no longer avoidable in cooperative systems since, without effective mitigation of the MAI, the diversity gains that lead to faster decrease in error probability will diminish due to the error floor laid by the MAI. In this section, we will discuss the use of MUD in uplink cooperative CDMA systems for networks with designated and shared relays, respectively.

6.2.1 Uplink CDMA with Designated Relays Consider an uplink cooperative network that consists of K sources (denoted by s1 , · · · , sK ), K relays (denoted by r1 , . . ., rK ), and a single destination d (i.e., the base station), as shown in Fig. 6.6. Each source, say sk , is served by only one designated relay (i.e., rk ), but may still cause interference at other relays due to the broadcast nature of the wireless medium. The sourcerelay pair can be viewed as two mutually cooperating users that take turns in acting as the source and the relay. The signature waveforms of the sources are not assumed to be orthogonal of each other but are assumed to be linearly independent1 . The lack of orthogonality among the signature waveforms will cause MAI at both the relays and the destination. Hence, MUD must be employed at both locations in order to preserve the cooperative gains. Specifically, let us consider a two-phase cooperative transmission scheme. In Phase I, each source transmits a sequence of M symbols, modulated by its unique signature waveform. Suppose that xk [m] is the symbol transmitted by 1 The linear independence will be satisfied in practice with high probability. However, when it is not satisfied, the MUD can still be treated similarly by using the pseudo-inverse whenever the lack of linear independence leads to non-invertible matrices.

6.2 Code-Division Multiple Access (CDMA)

239

source sk in the m-th symbol period. Then, the signal transmitted by source sk can be written as the continuous time function M  Psk xk [m]uk (t − mTs ),

xk (t) =

(6.20)

m=1

where Psk is the transmission power of source sk and uk (t) is the signature waveform of sk that is assumed to last over a symbol duration Ts . The signature waveform of source sk can be expressed mathematically as N 1  ck [n]ψ(t − nTc ), uk (t) = √ N n=1

(6.21)

where ck [n] ∈ {±1} is the n-th element of the spreading sequence assigned to source sk , N is the spreading gain, and ψ(t) is the normalized chip waveform with unit energy and chip duration Tc = Ts /N . Here, we assume that BPSK modulation is employed so that xk [m] ∈ {±1}. The results can be easily extended to systems with higher order modulations, e.g., QPSK or 64-QAM. Let us consider for simplicity the synchronous CDMA scenario where the users’ transmissions are assumed to arrive simultaneously at each receiver and are synchronized on the symbol level. Therefore, the the signal received at relay r and the destination d are given respectively by y (t) =

K M   Psk hk, xk [m]uk (t − mTs ) + w (t)

(6.22)

m=1 k=1

and (1)

yd (t) =

K M   (1) Psk fk,d xk [m]uk (t − mTs ) + wd (t),

(6.23)

m=1 k=1

where hk, and fk,d are the channel coefficients of the sk -r and the sk -d (1) links, respectively, and w (t) and wd (t) are the AWGN at relay r and destination d, respectively, both with mean 0 and autocorrelation Rwr (τ ) = 2 Rwd (τ ) = σw δ(τ ). The synchronous assumption is applicable when the users are relatively close to each other and the signal bandwidth is sufficiently small. Although a more practical study can be conducted based on the asynchronous scenario, the synchronous case is sufficient to demonstrate the effects of MAI and the benefits of using MUD in cooperative networks. In Phase II, each relay, say r , will decode and forward the symbols transmitted by its designated source s using the same signature waveform, i.e., u (t). By considering the uncoded DF relaying scheme, the symbol estimate x ˆ [m] forwarded by relay r at time m may possibly be incorrect due to the lack of error detection. Therefore, the signal received at the destination in

240

6 Cooperative Communications with Multiple Sources

Phase II can be written as (2)

yd (t) =

K M   (2) Pr g,d x ˆ [m]u (t − mTs ) + wd (t),

(6.24)

m=1 =1

where Pr is the transmission power of relay r , g,d is the channel coefficient (2) on the r -d link, and wd (t) is the AWGN with mean 0 and autocorrelation 2 Rwd (τ ) = σw δ(τ ). To detect the symbols at the relays, different receiver structures can be employed, including both single-user and multiuser detectors. The simplest of these schemes is the single-user matched filter (MF) detector, where the received signal, e.g., y (t), is passed through a filter matched to the signature waveform of the source of interest, e.g., u (t). The corresponding MF output during the m-th symbol period can be written as 

Ts

zMF [m] =

y (t + mTs )u (t)dt Psk ρk, hk, xk [m] + w [m], = Ps h, x [m] + 0

(6.25)

k=

where ρk, is the correlation between the signature waveforms uk (t) and u (t), i.e.,  Ts ρk, = uk (t)u (t)dt, 0

B Ts

and w [m] = 0 w (t+ mTs )u (t)dt is Gaussian distributed with mean 0 and 2 variance σw . The first term in (6.25) contains the signal of interest at relay r while the second term contains the MAI from other users. The signal-tointerference-plus-noise ratio (SINR) of the MF output zMF at relay r is given by SINR = 

Ps |h, |2 . 2 2 2 k= Psk ρk, |hk, | + σw

(6.26)

The single-user MF detector is optimal in AWGN channels but may experience significant performance degradations in the presence of MAI. The effect is particularly pronounced when interfering users are located closer to the destination or transmitting with much higher power than the source, resulting in the so-called near-far effect . To mitigate MAI, more sophisticated multiuser detectors (MUDs) can be employed at the receivers to jointly detect all sources’ symbols instead of treating the signals transmitted by other sources as interference. To perform joint detection over all sources’ symbols, the signal received at each relay, say relay r , can first be passed through a matched filter bank (MFB) consisting of K receive filters in parallel that are matched to the signature waveforms

6.2 Code-Division Multiple Access (CDMA)

241

u1 (t), u2 (t), . . ., uK (t). Suppose that x[m] = [x1 [m], x2 [[m], · · · , xK [m]]T is the vector of symbols transmitted by the K sources during the m-th symbol period. Then, the MFB output during this symbol period can be written as y [m] = RHs, x[m] + w [m], (6.27) + *where Hs, = diag Ps1 h1, , Ps2 h2, , · · · , PsK hK, is a diagonal matrix consisting of the transmit power times the channel coefficients between the sources and relay r , the matrix R is the correlation matrix given by ⎡ ⎤ ρ1,1 ρ1,2 · · · ρ1,K ⎢ ρ2,1 ρ2,2 · · · ρ2,K ⎥ ⎢ ⎥ (6.28) R=⎢ . . ⎥, .. .. ⎣ .. . . .. ⎦ ρK,1 ρK,2 · · · ρK,K and w [m] is the AWGN at the output of the MFB with zero-mean and 2 correlation matrix E[w [m]w [m]H ] = σw R. By assuming that each symbol is transmitted with equal probability, the optimal MUD that minimizes the average symbol error probability can be derived by the maximum likelihood (ML) criterion, where the detected symbol vector at relay r is given by ˆ  = arg max p (y [m]|x[m]) x x[m]

= arg min (y [m] − RHs, x[m]) R−1 (y [m] − RHs, x[m]) . H

(6.29)

x[m]

However, the possible values of x[m] and, thus, the complexity of the ML detector grows exponentially with the number of users, which is unaffordable to most mobile devices in practice. Alternatively, several suboptimal linear MUDs have been proposed to reduce MAI at a more reasonable computational cost. Two linear MUDs have been adopted the most in the literature: the decorrelating MUD [17] and the minimum mean square error (MMSE) MUD [18]. Specifically, in decorrelating MUDs, the received signal y [m] is multiplied by C = (RHs, )−1 to obtain zDEC [m] = C y = (RHs, )−1 y  :  [m], = x[m] + w

(6.30)

:  [m] = (RHs, )−1 w [m] is the effective noise at the output of the where w MUD. The received signal has been decorrelated in the sense that each element in zDEC depends only on the realization of one source’s symbol. Conse quently, zDEC can be viewed as K parallel single-user AWGN channels and  a decision on each source’s transmitted symbol can be made based only on the corresponding entry in zDEC . However, at the output of the decorrelating  receiver, the noise covariance matrix becomes

242

6 Cooperative Communications with Multiple Sources

−1 −1 −H −1 2 :  [m]w :  [m]H ] = H−1 E[w E[w [m]w [m]H ]R−1 H−H Hs, , s, R s, = σw Hs, R

which may yield large noise values if the matrix R is ill-conditioned. This is the so called noise enhancement problem similar to that experienced in zero-forcing equalizers (used to combat intersymbol interference). In MMSE MUDs, the linear filter C is chosen to minimize the MSE E |x[m] − C y [m]|2 . By taking the derivative of the MSE with respect to C and setting it to zero, we obtain C = Kxy K−1 yy ,

(6.31)

Kxy = E[x[m]y [m]H ] = HH s, R

(6.32)

2 Kyy = E[y [m]y [m]H ] = RHs, HH s, R + σw R.

(6.33)

where and The output of the MMSE MUD is given by [m] = C y [m]. zMMSE 

(6.34)

When relay r is designated to source s , it will only be interested in the symbol transmitted by source s and, thus, only the -th element of zMMSE [m]  is utilized for detection. The -th element of zMMSE [m] is given by  MMSE MMSE −1 z, [m] = eH [m] = eH  z  Kxy Kyy y [m] * +−1 H 2 = Ps h∗, eH y [m],  R RHs, Hs, R + σw R

(6.35) (6.36)

where e is the -th column of a K × K identity matrix. The resulting MSE is ( 2 ) MMSE MSE = E x [m] − z, [m] * +−1 H 2 = 1 − Ps |h, |2 eH Re  R RHs, Hs, R + σw R and the SINR at relay r can be computed as  −1 2 −1 Hs, HH Ps |h, |2 eH e  s, + σw R SINR =  −1 . 2 −1 Hs, HH 1 − Ps |h, |2 eH e  s, + σw R

(6.37)

The MMSE MUD minimizes the total interference plus noise power at the receiver and, thus, serves as a good compromise between noise reduction and interference mitigation. It is important to note that linear receivers described above can be followed by either a hard decision device if DF relaying is employed or by a analog amplifying devices if AF relaying is adopted. These operations are described

6.2 Code-Division Multiple Access (CDMA)

243

in detail in the following. For notational simplicity, we shall omit the index on m since MUD is performed on a symbol-by-symbol basis and the operations do not vary with m.

(i) MUD with Decode-and-Forward Relays When considering the DF scheme, we assume that each relay, say relay r , performs a hard decision to detect the symbol of its corresponding source, i.e., x ˆ = sgn(z, ), and forwards it to the destination in Phase II of the transmission process. By considering the uncoded case, we assume that the detected symbols are forwarded at the relays without performing any error detection. Therefore, the symbol forwarded by relay r can be expressed as x ˆ = θ x , where θ = −1 if the detection at relay r is incorrect and θ = 1 if it is correct. Here, θ ∈ {±1} can be modeled as a Bernoulli random variable with distribution Pr (θ = −1) = 1 − Pr (θ = 1) = ε , where ε is the detection error probability at relay r . By treating the interference as Gaussian, the error probability can be approximated by + *√ ε ≈ Q 2SINR , where the SINR is given by (6.26) if the single-user MF receiver is applied, or is given by (6.37) if the MMSE MUD is applied at the relays. ˆ = [ˆ Let x x1 , xˆ2 , · · · , x ˆK ]T be the vector consisting of the detected symbols of their corresponding sources at the K relays. At the destination, the signals received in Phase I and Phase II are passed through a MFB corresponding to the signature waveforms u1 (t), u2 (t), . . ., uK (t), which yield the outputs (1)

(1)

(2)

(2)

yd = RFs,d x + wd

(6.38)

and ˆ + wd , yd = RGr,d x (6.39) respectively, where Fs,d = diag( Ps1 f1,d , Ps2 f2,d , · · · , PsK fK,d), Gr,d = (1) (2) diag( Pr1 g1,d , Pr2 g2,d , · · · , PrK gK,d ), and wd and wd are the AWGNs 2 at the destination in the two phases with distribution CN (0K×1 , σw R). Recall that fk, and g,d are the channel coefficients of the sk -r and the r -d links. The signals received in both phases can be collected into the vector

244

6 Cooperative Communications with Multiple Sources (1)T

(2)T

Yd = [yd , yd ]T      (1)   wd Fs,d 0 x R 0 + = (2) ˆ x 0 Gr,d 0 R w

(6.40)

d

ˇ Hˇ ˇ x + w. ˇ R

(6.41)

By adopting the MMSE MUD at the destination, we can obtain the symbol estimate  −1  zMMSE = E xYdH E Yd YdH Yd , d * + ˇ HR ˇ R ˇ HK ˇ xˇ xˇ H ˇ HR ˇ + σ2 R ˇ −1 Yd (6.42) = Kxˇx H w where Kxˇ xˇ

 H ˇx ˇ = E x



I Θ Θ I

 (6.43)

and Θ = diag(1 − 2ε1 , 1 − 2ε2 , · · · , 1 − 2εK ). The symbols in x are then detected by performing a hard decision on each entry of zMMSE . d (ii) MUD with Amplify-and-Forward Relays When considering the AF scheme, the signal forwarded by relay r can be expressed as z, x ˆ = , E [|z, |2 ] where * +  2 −1 −1 Hs, HH e E |z, |2 = Ps |h, |2 eH  s, + σw R when the MMSE MUD is employed at the relay. Let us define the -th row of C in (6.31) as c, = E[x yH ] =

* +−1 H 2 Ps h∗, eH  R RHs, Hs, R + σw R

such that z, = c, y as in (6.36). Suppose that the signals received in both phases at the destination are also passed through the MFB, similar to the DF (1) case, which yields the output yd in Phase I given by (6.38) and the output in Phase II given by

6.2 Code-Division Multiple Access (CDMA) (2)

245

(2)

ˆ + wd , yd = RGr,d x ⎛⎡

⎡

⎤

⎤⎞

w1 RHs,1 ⎢ w2 ⎥⎟ ⎜⎢ RHs,2 ⎥ ⎢ ⎥ ⎥⎟ ⎜⎢ (2) = RGr,d ACr ⎜⎢ ⎥ x + ⎢ .. ⎥⎟ + wd , .. ⎣ . ⎦⎠ ⎦ ⎝⎣ . RHs,K wK

where

(6.44)

(6.45)

+− 1 * A = diag E[|z1,1 |2 ], E[|z2,2 |2 ], · · · , E[|zK,K |2 ] 2

and Cr = diag (c1,1 , c2,2 , · · · , cK,K ) . The MFB outputs of both phases can again be collected into the vector     (1) wd RFs,d x+ Yd = (2) , RGr,d ACr Ξ ˇ r + wd RGr,d ACr w ⎡

where

⎢ ⎢ Ξ=⎢ ⎣

RHs,1 RHs,2 .. .

⎤ ⎥ ⎥ ⎥ ⎦

RHs,K ˇ r = [w1T , w2 T , · · · , wK ]T is a K 2 × 1 noise vector with zero mean and and w 2 correlation matrix Rw ˇ r = σw diag(R, R, · · · , R). By employing the MMSE MUD at the destination, we can also obtain the symbol estimate  −1  zd = E xYdH E Yd YdH Yd , where

  H H H E xYdH = FH s,d R Ξ Cr AGr,d R

and  E Yd YdH  2 RFs,d FH s,d R + σw R = H RGr,d ACr ΞFs,d R

(6.46)

 H RFs,d ΞH CH r AGr,d R . H H 2 RGr,d ACr (ΞΞH+Rw ˇ r )Cr AGr,d R+σw R (6.47)

In Fig. 6.7 (obtained from [24], the BER performance of the single-user MF receiver and the MMSE MUD is shown for both the DF and the AF scenarios. Here, the system consists of 15 sources, 4 of which are served by their respective relays and the remaining 11 sources transmit directly to the BS without cooperation. The spreading gain is set as N = 15. The noncooperative (NC) case, where no users are served by relays, and the ideal cooperative (IC) case, where the source symbols are assumed to be available

246

6 Cooperative Communications with Multiple Sources

Fig. 6.7 BER performance of cooperative users in cooperative CDMA uplink systems c with designated relays (From Venturino, Wang and Lops; [2006] IEEE ).

at the relays, are also shown for comparison. We can observe that, in the DF case, no spatial diversity gain is achieved by employing the single-user MF receiver at the relays since the relay errors caused by the MAI will eventually dominate the overall BER. On the other hand, in the AF case, no hard decision is made at the relays and, thus, diversity gains can be preserved by employing MMSE MUD at the destination, even when the MF receiver is used at the relays. However, since the MAI is not suppressed at the relays if the single-user MF receiver is adopted, a large part of the relay power may be used to forward interference instead of the source’s message, resulting in loss of coding gains. When the MMSE receiver is employed at the relays, spatial diversity gain can be achieved for both AF and DF systems. So far, we assumed that full CSI is available at the relays when deriving the multiuser detectors. However, it is often prohibitive in practice to acquire the channel of all source-relay links. When only partial CSI is available (e.g., only the statistics of the channel are known), blind techniques, such as the blind MMSE estimator proposed in [24], can be applied alternatively to alleviate the MAI. Readers are referred to [24] for further details.

6.2 Code-Division Multiple Access (CDMA)

247

j) k)

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'HVWLQDWLRQ

ˁ ʳʳ ˁ

ʳˁ ˁʳʳˁʳ

jD

kC

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Fig. 6.8 Illustration of a K-source CDMA uplink system with L shared relays.

6.2.2 Uplink CDMA with Shared Relays In the previous subsection, we assumed that each relay only forwards messages for a certain designated source. Therefore, the MUD at each relay is used only to detect the symbol of its corresponding source and the signals transmitted by other sources are suppressed. However, with designated relays, the spatial diversity provided by the multiple relay paths and the relays ability to jointly process the sources’ messages are not fully utilized. In this subsection, we consider instead the case of shared relays, where all relays can together forward the messages of all sources. In this case, the messages received from different sources can be jointly processed and forwarded to the destination, instead of treating other sources’ signals as interference [13]. Consider a cooperative network with K source users (i.e., s1 , s2 , · · · , sK ) and L relay stations (i.e., r1 , r2 , · · · , rL ) that together forward messages from the sources to the destination as shown in Fig. 6.8. Since no relay is designated to any particular source, there is no constraints on the values of L and K. Again, we assume that the cooperation scheme takes on two phases of transmission and that all sources transmissions are synchronized (i.e., the synchronous CDMA scenario [25]). In Phase I, each source, say sk , transmits a block of M symbols using its unique spreading waveform uk (t). Then, similar to the previous case, the signals received at the relays and the destination are passed through the MFB with filters matched to the spreading waveforms of the K sources, i.e., u1 (t), u2 (t), · · · , uK (t), resulting in the MFB output given by y [m] = RHs, x[m] + w [m] and (1)

(1)

yd [m] = RFs,d x[m] + wd [m],

248

6 Cooperative Communications with Multiple Sources

respectively, where Fs,d = diag( Ps1 f1,d , Ps2 f2,d , · · · , PsK fK,d ), Hs, = diag( Ps1 h1, , Ps2 h2, , · · · , PsK hK, ), R is the correlation matrix of the (1) spreading waveforms, and w [m] and wd [m] are the AWGNs at relay r and 2 destination d with mean 0K×1 and correlation matrix σw R. The MFB output at the relays can then be used to perform an MMSE estimate on the sources’ symbols as given in (6.34). Depending on whether or not a hard decision is made at the relays based on the MMSE estimate, DF and AF relaying schemes can be adopted. In the following, we shall focus our discussions on the DF case while the AF case can be devised similarly. Suppose that the MMSE MUD is employed at the relays to detected the ˆ  [m] = [ˆ sources’ symbols. The vector x x,1 [m], . . . , x ˆ,K [m]]T is used to denote the vector of symbols detected at relay r in the m-th symbol period, with the k-th element correspond to that of source sk . The symbols detected over the M symbol periods of a data block can be collected into the matrix ˆ  = [ˆ ˆ  [M ]]. The advantage of having shared relays is that each X x [1], · · · , x source can now be served by multiple relays and the cooperative strategies introduced for multi-relay systems in Chapter 4 can be employed. In general, the symbols forwarded by relay r can be expressed as ˆ  ) = [t [1], · · · , t [M  ]], T = κ(X ˆ  . The function κ(·) dewhich is a function of the detected symbol matrix X pends on the specific cooperation strategy that is employed and, thus, will be referred to as the cooperative function. For example, if transmit beamformˆ , ing is performed, the transmitted symbols can be expressed as T = B X where B = diag(β,1 , . . . , β,K ) and β,k is the beamforming coefficient imposed by r on source sk ’s symbols. The set of coefficients β1,k , β2,k , . . ., βL,k across the L relays form the set of beamforming coefficients for the relaying of sk ’s symbols, as described in Chapter 4.2. Selective relaying schemes can be expressed similarly with β,k > 0 if r is chosen to forward sk ’s symbol and β,k = 0, otherwise. Given the cooperation scheme, the m-th column of T , i.e., t [m] = [t,1 [m], · · · , t,K [m]]T , can be viewed as the vector of symbols transmitted by relay r during the m-th symbol period. The k-th element t,k [m] will be transmitted using the spreading waveform of source sk , i.e., uk (t). The set of cooperative functions has to satisfy power constraint L L H =1 Pr = =1 E[t [m] Rt [m]] = Pr . The signal received at destination d during Phase II is given by 

(2) yd (t)

=

K  M  L 

(2)

g,d t,k [m]uk (t − mTs ) + wd (t).

(6.48)

m=1 k=1 =1

By passing the signal through the MFB, we will obtain, in the m-th symbol period, the MFB output

6.2 Code-Division Multiple Access (CDMA) (2)

yd [m] =

L 

249 (2)

g,d Rt [m] + wd [m],

(6.49)

=1 (2)

2 R). The MFB outputs obtained over the M where wd [m] ∼ CN (0K×1 , σw symbol periods can be collected into the matrix

(2)

Yd =

L 

(2)

g,d RT + Wd ,

(6.50)

=1 (2)

(2)

(2)

where Wd = [wd [1], · · · , wd [M ]]. By employing MMSE MUD at both the relays and the destination, MAI can be reduced up to a certain degree, but may still dominate the BER performance at high SNR. However, since the relays are able to obtain an estimate of all sources’ symbols, precoding can be performed at the relays to further enhance the MUD’s ability to mitigate MAI. With proper design of the relay precoder, the sources’ signals can be decorrelated at the destination without noise enhancement, which is achieved by the so-called relay-assisted decorrelating multiuser detector (RAD-MUD) [13] described in the following.

Relay-Assisted Decorrelating MUD Conventionally, signal decorrelation have been performed by imposing a decorrelating filter R−1 either at the destination or at the relays. The former scheme leads to the decorrelating MUD which has been shown to cause noise enhancement at the destination. The latter scheme is referred to as the zero-forcing precoder [27] where R−1 is used to precompensate for the MAI that is to occur over the relay-to-destination link. However, the ZF precoding scheme results in power amplification at the relays that degrades performance when the relay power is constrained. The two schemes perform identically under the same relay power constraint. In contrast to these schemes, RADMUD performs half the decorrelating operation at the relays and half at the destination [13]. By doing so, both the noise enhancement and the power amplification problems will be avoided, as we show in the following. The key idea behind RAD-MUD is to decompose the matrix R−1 into two identical matrices and distribute them among the relays and the destination, respectively. The block diagram of the RAD-MUD is illustrated in Fig. 6.9. Specifically, when R is full rank, we can take the Cholesky decomposition [12] such that R = LLH , where L is a K × K lower triangular matrix. Then, at each relay, the output ˆ  ) is pre-multiplied by the matrix L−H before of the cooperative function κ(X being forwarded to the destination. Therefore, the transmitted symbol matrix

250

6 Cooperative Communications with Multiple Sources

Fig. 6.9 The block diagram of RAD-MUD for multiuser forwarding CDMA systems. c (From Huang, Hong and Kuo with modified notations; [2008] IEEE ).

can now be expressed as

ˆ  ). T = L−H κ(X

(6.51)

By (6.50), the MFB output at the destination can be expressed as (2)

Yd =

L 

ˆ ) + W . g,d RL−H κ(X d (2)

(6.52)

=1

Then, the received signal in (6.52) is multiplied by L−1 (i.e., the other half of R−1 ), which yields the output ˘ (2) = L−1 Y(2) = Y d d

L 

(2)

ˆ ) + W ˘ , g,d κ(X d

(6.53)

l=1 (2) (2) (2) ˘ (2) = L−1 W(2) = [w ˘ d [1], · · · , w ˘ d [M ]]. The noise term w ˘ d [m] = where W d d (2) L−1 wd [m] now has covariance matrix ) ( (2) (2) 2 ˘ d [m]H = σw ˘ d [m]w IK×K E w

and, thus, L−1 can be viewed as a pre-whitening filter at the destination. Since the noise covariance matrix does not depend on R−1 , the noise enhancement problem is avoided. Moreover, it is also possible to show that the power of the signal transmitted by the relays does not depend on R−1 (see [13]) and, thus, the power amplification problem is also avoided. By employing the RADMUD scheme, the relay-to-destination channel is effectively decomposed into K orthogonal channels, one for each source’s signal, and, thus, the multirelay cooperation strategies proposed in Chapter 4 can be employed without interference from other sources. However, it is worthwhile to note that, with limited resources at the relays, the sources may still need to compete for resources at the relays, which may again affect their performance.

6.2 Code-Division Multiple Access (CDMA)

251

Finally, the signals received at the destination in Phases I and II, i.e., (1) (1) (1) ˘ (2) , can be combined to detect the sources’ Yd = [yd [1], . . . , yd [M ]] and Y d symbols. For simplicity, let us consider the case where the cooperative functions are linear, such as the case of transmit beamforming or selective relayˆ  ) = B X ˆ ing. In these case, the cooperative function can be written as κ(X with B = diag(β,1 , · · · , β,K ) and, thus, only the signals received in the m(1) (2) ˘ d [m], will contain information regarding th symbol period, i.e., yd [m] and y the source symbol x[m]. These signals can be combined into the single vector       (1) (1) RHs,d wd [m] yd [m] x[m] + yd [m] = = L , (6.54) (2) (2) ˘ d [m] ˘ d [m] y w =1 g,d B Θ where Θ = diag(θ,1 , · · · , θ,K ) with θ,k ∈ {−1, 1} being a Bernoulli random variable indicating whether or not the symbol xk [m] is correctly decoded at the relay r . Similar to the case with designated relays (see Section 6.2.1), we have Pr(θ,k = −1) = 1 − Pr(θ,k = −1) = ε,k , where ε,k is the detection error probability. By treating the interference as noise, the error probability can be approximated as ⎛; ⎞ <  −1 < H H 2 2 −1 ek ⎟ ⎜< Psk |hk, | ek Hs, Hs, + σw R @

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Fig. 7.2 Example of the FR scheme with N = 3 subcarriers per user. Here, Zi represents the retransmission of the signal Xi [n] on subcarrier (j, k).

[n]

sion power per user in Phase I, which is assumed to be equally distributed over all subcarriers, i.e., E[|Xi [n]|2 ] = PI /N for i = 1, 2. In Phase II, each user will either relay the data of its partner or retransmit its own data on its N subcarriers. Let us first consider the intuitive approach where the data of both users on all subcarriers, i.e., X1 [0], . . . , X1 [N − 1] and X2 [0], . . . , X2 [N − 1], are forwarded by all 2N subcarriers in orthogonal time slots of Phase II, similar to the repetition-based approach proposed in [8]. This is done by assigning 2N equal length time slots to Phase II and by dedicating each time slot to the relaying or retransmission of a different subcarrier’s data. We shall refer to this as the Full Repetition (FR) scheme. An example with N = 3 subcarriers per user is illustrated in Fig. 7.2. Here, (j,k) Zi [n] represents the signal transmitted on subcarrier (j, k) when forwarding data Xi [n]. Let PII be the total transmission power per user in Phase II and let (j,k) αi [n]PII be the power assigned to subcarrier (j, k) for the relaying or retransmission of the data Xi [n]. When data Xi [n] is forwarded on subcarrier (j, k), the transmitted signal is given by ⎧ (j,k) ⎪ α [n]PII ⎪ · Yj [i, n], if j = i, ⎨ PI i 2 2 |H (j,k) i,j [n]| +σw N  Zi [n] = (7.4) (j,k) ⎪ ⎪ ⎩ αi [n]PII · Xi [n], if j = i, PI /N and the signal received at the destination is (j,k)

Yd [j, k] = Hj,d [k]Zi

[n] + Wd [j, k].

Notice that the power assigned to the transmissions in Phase II must satisfy the per-user power constraint that

276

7 Cooperation Relaying in OFDM and MIMO Systems −1 −1 N 2 N   

(j,k)

αi

[n] · PII = PII

i=1 n=0 k=0

or

−1 −1 N 2 N   

(j,k)

αi

[n] = 1,

(7.5)

i=1 n=0 k=0

for j = 1, 2. Let us define the power allocation matrix as ⎡

⎤ (1,0) (1,0) (1,0) [0] · · · α1 [N − 1] α2 [0] · · · α2 [N − 1] ⎢ ⎥ .. .. .. .. .. .. ⎢ ⎥ . . . . . . ⎢ ⎥ (1,N −1) (1,N −1) (1,N −1) ⎢ (1,N −1) ⎥ [0] · · · α1 [N − 1] α2 [0] · · · α2 [N − 1] ⎥ ⎢ α1 A = ⎢ (2,0) ⎥ (2,0) (2,0) ⎢ α1 [0] · · · α(2,0) [N − 1] α2 [0] · · · α2 [N − 1] ⎥ 1 ⎢ ⎥ ⎢ ⎥ .. .. .. .. .. .. ⎣ ⎦ . . . . . . (2,N −1) (2,N −1) (2,N −1) (2,N −1) α1 [0] · · · α1 [N − 1] α2 [0] · · · α2 [N − 1]   A1,1 A1,2 = , (7.6) A2,1 A2,2 (1,0)

α1

where submatrix Aj,i consists of the portions of power allocated to user j for relaying of user i’s data (if j = i) or for the retransmission of its own data (if j = i). The sum of each column is the total portion of power allocated to the transmission of a certain subcarrier’s data in Phase II. By assuming that the source symbol is Gaussian, an achievable sum rate of the AF cooperative OFDM system, under power allocation A, is given by C(A) =

2 N −1 Δf   log (1 + SNRi [n](A)) , 2N + 1 i=1 n=0

(7.7)

where SNRi [n](A) =

N −1  1 (i,k) |Hi,d [n]|2 γI + αi [n]|Hi,d [k]|2 γII N k=0   N −1  1 (j,k) |Hi,j [n]|2 γI , αi [n]|Hj,d [k]|2 γII + g N k=0

2 is the effective SNR corresponding to data Xi [n], γI = PI /σw Δf , γII = 2 PII /σw Δf and g(x, y) = xy/(x + y + 1). Notice that the rate is scaled by Δf /(2N + 1) since a total of 2N + 1 time slots in Phases I and II are utilized for transmission. When the CSI is unknown at the transmitters, the power in (j,k) Phase II can be equally allocated to all subcarriers such that αi [n] = 2N1 2 for all i, j ∈ {1, 2} and k, n ∈ {1, 2, . . . , N }. On the other hand, when full CSI is available at the transmitters, optimal power allocation can be found by solving the following optimization problem:

7.2 Resource Allocation in Pair-Wise Cooperative OFDM Systems

minimize

−

−1 2 N  

* + log 1 + SNRi [n](A)

277

(7.8)

i=1 n=0

subject to

1TN Aj 12N = 1, for j ∈ {1, 2} − A  02N ×2N

where Aj = [Aj,1 , Aj,2 ], 02N ×2N is the 2N × 2N all-zero matrix and  is the element-wise inequality and 1M is of M × 1 all one vector. It has been shown in [20] that the optimization problem is convex with respect to A and, thus, can be solved via standard convex optimization tools [3]. More interestingly, by KKT conditions, the following properties can also be observed, as shown in [20]. Lemma 7.1. Let A∗ be the optimal power allocation with respect to (7.8). The following properties must hold: (i) If mink SNRj [k](A∗ ) ≤ minn SNRi [n](A∗ ), for j = i, then A∗j,i = 0N ×N . (ii) Let n∗i  arg max |Hi,d [k]|2 be the index of user i’s subcarrier that has the k

(i,k)∗

best uplink channel. It must hold that αi

[n] = 0, ∀n and ∀k = n∗i .

The result in Lemma 7.1(i) implies that user j should not relay for user i if its minimum effective SNR over all subcarriers is less than that of user i. That is, the optimal power allocation allows only one user to relay for the other user when cooperation exists. However, it could be that no cooperation exists, in which case, we have A∗j,i = A∗i,j = 0. Moreover, the result in (ii) implies that, when user i is retransmitting its own data in Phase II, only the subcarrier with the most favorable channel should be utilized. To determine when cooperation is desirable, one can compare the maximum achievable rate between cooperative and non-cooperative systems. Specifically, when no cooperation is employed (i.e., when Aj,i and Ai,j are set to zero), the optimal power allocation A∗i,i results in the following waterfilling solution ,  + 1 Ui − γNI |Hi,d [n]|2 , if k = n∗i , (i,k)∗ 2 γ |H [k]| II i,d [n] = αi 0, otherwise, for n = 1, . . . , N , where [x]+ = max(0, x). Here, Ui is the water-level associated with user i and is chosen to satisfy the power constraint in (7.5). The effective SNR of the data associated with subcarrier (i, n) is thus given by

if γNI |Hi,d [n]|2 < Ui U, SC NRi [n] = γIi (7.9) 2 N |Hi,d [n]| , otherwise. More interestingly, it has been shown in [20] that the cooperative relationship between users i and j can be simply determined by comparing their respective

278

7 Cooperation Relaying in OFDM and MIMO Systems

water-levels Ui and Uj , which can be viewed as the effective SNR achievable by the users under a given total power constraint. Proposition 7.1 ([20]). Let Ui and Uj be the water-levels associated with the non-cooperative power allocations of users i and j. The following properties must hold: (i) If Ui ≤ Uj for i = j, the optimal power allocation yields A∗i,j = 0, i.e., user i does not relay for user j. (ii) Given Ui ≤ Uj , for i = j, cooperation achieves a higher system sum rate if and only if F (j, i) > 0, where 

 N F (j, i) = 1 + Uj − min (1 + Ui ) 1 + , γI max∈Si |Hi,j [ ]|2   0 γI N 1+ minc 1 + |Hi,d [ ]|2 ∈Si N γI |Hi,j [ ]|2 with Si = {n :

γI 2 N |Hi,d [n]|

< Ui } and Sic = {0, . . . , N − 1} − Si .

The result in Proposition 7.1(i) implies that user i should not relay for user j if its non-cooperative water-level is less than that of user j, i.e., Ui ≤ Uj . This is reasonable since one should not consume power in forwarding other user’s data if the total power is not sufficient to achieve a better effective SNR for itself. However, this does not imply that user j should relay for user i. In fact, the result in Proposition 7.1(ii) further shows that cooperation is desirable if and only if the value F (j, i) is greater than 0. One can argue that the value of F (j, i) in some sense characterizes the advantage of cooperation and, thus, can serve as a useful criterion for partner selection, as elaborated in [20]. The scheme discussed in this section is a simple and intuitive way of exploiting spatial and frequency diversity in cooperative OFDM systems, but is not bandwidth efficient due to the large number of repetition time slots needed in Phase II. A more bandwidth efficient scheme allocates only one time slot to Phase II and allows each subcarrier to forward only one subcarrier’s data in this time slot. However, this results in the so-called subcarrier matching problem that determines the data that should be relayed or retransmitted by each subcarrier in Phase II.

7.2.2 Subcarrier Matching for Pair-Wise Cooperative OFDM Systems In this section, we consider the subcarrier matching problem for pair-wise cooperative OFDM systems where each subcarrier is only allowed to relay or retransmit the data of one other subcarrier in Phase II. That is, only one time slot is available in Phase II as illustrated in the example of Fig. 7.3.

7.2 Resource Allocation in Pair-Wise Cooperative OFDM Systems

279

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Fig. 7.3 Example of the subcarrier matching problem for a system with N = 3 subcarriers per user. Here, the subcarrier matching function π is defined by π(1, 0) = (2, 1), π(1, 1) = (2, 2), π(1, 2) = (1, 0), π(2, 0) = (1, 2), π(2, 1) = (2, 0), and π(2, 2) = (1, 1).

Let S = {1, 2} × {0, 1, . . . , N − 1} be the index set of all subcarriers in the cooperative pair and let π : S → S be the subcarrier matching function, where π(i, n) is the index of the subcarrier whose data is forwarded by subcarrier (i, n). Given the subcarrier matching π and the power allocation Q = [Q(1,0) , . . . , Q(1,N −1) , Q(2,0) , . . . , Q(2,N −1) ]T , where Q(i,n) is the power allocated to subcarrier (i, n) in Phase II, an achievable sum rate is given by ⎛ ⎞ −1 2 N    Δf γI (j,k) C(π, Q) = log ⎝1 + |Hi,d [n]|2 + ζi [n]⎠ (7.10) 2 i=1 n=0 N −1 (j,k)∈π

(i,n)

where π −1 (i, n) is the set of subcarriers that are assigned to relay or retransmit Xi [n] (i.e., the data of subcarrier (i, n)) and

γI g( N |Hi,j [n]|2 , Q(j,k) |Hj,d [k]|2 γII ), for j = i, (j,k) ζi [n] = for j = i, Q(j,k) |Hj,d [k]|2 γII , (n)

is the effective SNR of data Xi contributed by the transmissions of subcarrier (j, k) in Phase II. The optimal power allocation Q and the subcarrier allocation π can be jointly determined by maximizing the sum rate in (7.10). However, this problem is known to be NP-hard [7,24] and, thus, is prohibitive in practice since the actual value of N may be large. Hence, efficient suboptimal algorithms must be devised to solve the subcarrier matching problem. Interestingly, the optimal power allocation A∗ of the FR scheme derived in the previous subsection can be utilized to obtain an efficient solution for this problem. The basic idea is to assign the subcarriers in Phase II to the data that is allocated the most power in A∗ . The FR-based subcarrier matching algorithm is described below and summarized in Fig. 7.4.

280

7 Cooperation Relaying in OFDM and MIMO Systems SUBCARRIER MATCHING ALGORITHM 1: FIND order-mappings φ1 , φ2 : {0, . . . , N −1} → {0, . . . , N −1} 2: such that |Hi,d [φi (0)]|2 ≤ · · · ≤ |Hi,d [φi (N − 1)]|2 ; ∗ 3: IF A1,2 = A∗2,1 = 0 THEN 4: FOR n = 0 to N − 1 5: π(i, φi (N − n − 1)) := (i, φi (n)), for i = 1, 2; 6: END 7: ELSE 8: IF A∗1,2 = 0 THEN i := 1 and j := 2; 9: ELSE i := 2 and j := 1; 10: END 11: SET Us = Ur := {0, 1, ..., N − 1} and maxA = ∞; 12: WHILE |Ur | > 0 and maxA > 0  (j,k)∗   13: maxA := maxk∈Ur max(i ,n )∈S αi [n ] ;





14: indA := arg maxk∈Ur max(i ,n )∈S αi [n ] ; 15: IF maxA > 0 THEN (j,indA)∗  16: π(j, indA) = arg max(i ,n )∈S αi [n ]; 17: Ur := Ur − {indA}; 18: IF π(j, indA) = (j, n ) for some n  THEN Us := Us − {n }; END 19: END 20: END 21: FIND order-mapping φj : {0, . . . , |Ur | − 1} → Ur such that 22: |Hj,d [φj (0)]|2 ≤ · · · ≤ |Hj,d [φj (|Ur | − 1)]|2 23: and order-mappings ϕi : {0, . . . , N − 1} → {0, . . . , N − 1}, 24: ϕj : {0, . . . , |Us | − 1} → Us such that 25: SNRi [ϕi (0)]≤ · · · ≤SNRi [ϕi (N −1)], SNRj [ϕj (0)]≤ · · · ≤SNRj [ϕj (|Us |−1)] 26: where SNRi [n] is the effective SNR of Xi [n] given in (7.11); 27: FOR n = 0 to |Ur | − 1 28: π(j, φj (|Ur | − n − 1)) := (j, ϕj (n)); 29: END 30: FOR n = 0 to N − 1 31: π(i, φi (N − n − 1)) := (i, ϕi (n)); 32: END 33: END (j,k)∗

Fig. 7.4 FR-based subcarrier allocation algorithm. (Figure regenerated and modified from c Sung, Hong, and Chao. 2010 IEEE.)

Specifically, at the beginning of the algorithm, one can first check the conditions of Proposition 7.1 to determine whether or not cooperation is desirable. In cases where no cooperation exists (i.e., A∗1,2 = A∗2,1 = 0), the proposed subcarrier matching scheme assigns each of the user’s own data to one of its own subcarriers in Phase II according to the reverse order of their uplink channel qualities, e.g., the data originally transmitted on the worst subcarrier in Phase I is retransmitted by the best subcarrier of the same user in Phase II, as shown in lines 4–6. On the other hand, when A∗j,i = 0 (which implies that A∗i,j = 0), we start out by assigning data to the subcarriers of the cooperative user, i.e., user j, so that the data associated with the largest component in A∗j,i will be allocated the desired subcarrier in Phase

7.2 Resource Allocation in Pair-Wise Cooperative OFDM Systems

281

II. However, if the data belongs to the same user, we impose a restriction where no other of its own subcarriers will be allowed to retransmit the same data (c.f. lines 12–20 in Fig. 7.4). Then, the remaining subcarriers of user j that were not assigned any data at this point are then utilized to retransmit its own data. To do this, the effective SNR of each subcarrier of user j is first computed according to the contributions of the direct transmission in Phase I and part of the subcarrier matching determined up to this point. Then, the subcarriers’ messages are assigned to the remaining subcarriers in Phase II so that the message with the smallest effective SNR is retransmitted by the subcarrier with the best uplink channel, so on and so forth. This is done similarly at user i, who only retransmits its own data in Phase II. These procedures are described in lines 21–32. Notice that the effective SNRs on lines 25 and 26 are given by  γI (j,k) SNRi [n] = |Hi,d [n]|2 + ζi [n], (7.11) N −1 (j,k)∈π

(i,n)

for all i, n. After the subcarrier matching procedure has been completed, the optimal power allocation among subcarriers in Phase II, i.e. Q, can then be computed using standard convex optimization tools. Example: Comparison of Different Subcarrier Allocation Algorithms Following the IEEE 802.11a standard [15], let us consider the case where N = 64, Δf = 312.5 KHz, and the sampling period ts = 50 ns. We adopt the ν-tap exponential delay multipath channel model [15] with a root mean squared (RMS) delay spread of TRMS = 100 ns. Let hi,j be the channel between users i and j that consists of ν independent zero-mean complex 2 2 Gaussian random variables with variances σi,j [k] = σi,j [0]e−kts /TRMS , for k = 2 [0] is chosen according to 0, . . . , ν − 1, where ν = 10 × TRMS /ts and σi,j ν−1 2 2 the given value of E[ hi,j ] = k=0 σi,j [k]. We consider two sets of channel conditions: in Case I, we assume that the channels of both users are symmetric and set E[ h1,d 2 ] = E[ h2,d 2 ] = 1 and E[ h1,2 2 ] = E[ h2,1 2 ] = 2; in Case II, we assume that user 2 has a much worse channel to the destination and set E[ h2,d 2 ] = 0.2 while the other parameters remain the same. The frequency responses H1,d [n], H2,d [n], H1,2 [n], and H2,1 [n], for all n, are then obtained by taking the DFT of h1,d , h2,d , h1,2 , and h2,1 , respectively. Here, we assume that PI = PII = P . In Fig. 7.5, the sum rate for a cooperative pair is shown for different subcarrier allocation policies with optimal power allocation. Here, the SNR 2 is defined as the average power over the noise variance, i.e., P/(σw N Δf ). In this experiment, we consider 3 subcarrier matching schemes: (i) the FR-based subcarrier matching (FRSM), (ii) the random subcarrier matching (RSM) scheme, where each subcarrier randomly selects a subcarrier’s data to relay or retransmit, and (iii) the ordered subcarrier pairing (OSP) scheme [6, 9],

282

7 Cooperation Relaying in OFDM and MIMO Systems  )560 560 263

6XP5DWHELWVVHF+]



&DVH



&DVH

















615 3σZ1ΔI G%

Fig. 7.5 Comparison of the sum rate of a cooperative pair under different subcarrier allocation schemes and with optimal power allocation. The solid lines are for Case I and the dashed lines for Case II.

where subcarriers with the best uplink channels are used to relay for the data received on the best interuser channels. We observe from Fig. 7.5 that, for a sum rate of 4 bits/sec/Hz, in Case I a gain of approximately 2 dB is achieved with FRSM compared to RSM and OSP schemes, while in Case II FRSM achieves approximately 0.8 dB gain over OSP and 1.4 dB over RSM. We can observe that, when the statistics of the two users’ channels are identical (e.g., in Case I), the competition among users is more severe than that of the asymmetric case and, thus, the performance gains obtained with FRSM in Case I is larger than that in Case II.

7.3 Cooperative OFDM Systems with Multiple Relays The use of OFDM in cooperative systems can also be extended to the case with multiple relays. Similar to that discussed in Chapter 4, the relays in this scenario can form a virtual antenna array, employing different MIMO transmission techniques such as beamforming, antenna selection, space-time coding etc. With the additional degrees of freedom in the frequency domain, further processing or coding across subcarriers can be performed at the relays

7.3 Cooperative OFDM Systems with Multiple Relays

283

to enhance performance. In this section, we will describe basic distributed beamforming and selective relaying schemes in cooperative OFDM systems and introduce the use of distributed space-frequency codes in the next section.

7.3.1 Cooperative Beamforming for OFDM Multi-Relay Systems

Fig. 7.6 Illustration of cooperative OFDM systems with multiple relays.

Consider a cooperative OFDM system that consists of one source, L relays, and one destination, as shown in Fig. 7.6. The bandwidth is divided into N subcarriers with spacing equal to Δf . Similarly, cooperation is achieved in two phases: in Phase I, the source transmits a signal to all relays while, in Phase II, the relays transmit cooperatively to the destination using beamformed transmission. No direct link between the source and the destination is considered. In this subsection, we shall focus only on the AF scheme since the DF scheme follows more closely to that in Chapter 4. Specifically, let hs, = [hs, [0], . . . , hs, [νs, − 1]]T be the channel vector between the source and relay and let h,d = [h,d [0], . . . , h,d [ν,d − 1]]T be the channel vector between relay and the destination. Suppose that the source broadcasts a time-domain symbol vector x = [x[0], . . . , x[N − 1]]T to all relays. The signal received at relay can be expressed as y = CM(hs, )x + w ,

(7.12)

284

7 Cooperation Relaying in OFDM and MIMO Systems

where w = [w [0], . . . , w [N − 1]]T ∼ CN (0N ×1 , σr2 IN ×N ) is the noise vector at relay . The equivalent frequency-domain signal model is given by Y [n] = Hs, [n]X[n] + W [n], where

for n = 0, . . . , N − 1,

(7.13)

νs, −1

Hs, [n] =



hs, [k]e−j2πkn/N , for n = 0, . . . , N − 1,

k=0

is the frequency response of the channel and X[n] is the data transmitted on the n-th subcarrier with power E[|X[n]|2 ] = Ps . When perfect CSI is available at the relays, the transmitted symbol at each relay can be multiplied by a different complex coefficient to compensate for the known channel effects, achieving the so-called cooperative beamforming.

Frequency-Domain Cooperative Beamforming The most intuitive way to achieve beamforming gains in a cooperative OFDM system is by performing frequency-domain (FD) cooperative beamforming where DFT is performed at the relays to obtain the frequency-domain signals and beamforming coefficients are imposed separately on each subcarrier’s signal. More specifically, at relay - , the signal received on each subcarrier will first be scaled by β [n] = 1/ |Hs, [n]|2 Ps + σr2 and then multiplied by the beamforming coefficient G [n]. In this case, the total relay power is given by N −1  L 

E[|G [n]β [n]Y [n]|2 ] =

n=0 =1

N −1  L 

|G [n]|2 .

n=0 =1

The signal received at the destination on subcarrier n is given by Yd [n] =

L 

H,d [n]G [n]β [n]Y [n] + Wd [n]

=1

=

L  =1

H,d [n]G [n]β [n]Hs, [n]X[n] +

L 

H,d [n]G [n]β [n]W [n] + Wd [n]

=1

(7.14) where Wd [n] is the AWGN with variance σd2 . Let G[n] = [G1 [n], . . . , GL [n]]T be the beamforming vector across relays on the n-th subcarrier and let Hs,r [n] = [Hs,1 [n], . . . , Hs,L [n]]T , Hr,d = [H1,d [n], . . . , HL,d [n]]T be the channel vectors between the source and the relays and between the relays and the destination, respectively. Define Heff [n] = [H1,d [n]β1 [n]Hs,1 [n], . . . , HL,d [n]βL [n]Hs,L [n]]T

7.3 Cooperative OFDM Systems with Multiple Relays

285

as the effective channel vector of the end-to-end link. In this case, the received signal can be recast as : : r,d [n]B[n]W Yd [n] = G[n]T Heff [n]X[n] + G[n]T H r [n] + Wd [n], : : r,d [n] = diag(Hr,d [n]), B[n] = diag(β1 [n], . . . , βL [n]), and Wr [n] = where H T [W1 [n], . . . , WL [n]] . The SNR of the signal received on the n-th subcarrier is given by SNR[n] =

Ps G[n]T Heff [n](Heff [n])H G[n]∗ ( ) , (7.15) H σ 2 + (σ 2 /a[n])I G[n]∗ : r,d [n]B[n]( : : r,d [n]B[n]) : G[n]T H H r d

where a[n] = G[n] 2 is the squared gain of the beamforming vector. By assuming Gaussian inputs, the achievable rate on subcarrier n is C[n] =

1 log2 (1 + SNR[n]). 2

−1 The optimal beamforming vectors {G[n]}N n=0 can be derived by maximizing the sum rate over all subcarriers subject to a total power constraint at the relays. The optimization problem can be formulated as follows:

maximize

N −1  n=0

subject to

N −1 

C[n] =

N −1 * + 1  log 1 + SNR[n] 2 n=0

(7.16)

G[n] 2 ≤ Pr

n=0

where Pr is the total relay power constraint. Suppose that the beamforming vector G[n] can be expressed as G[n] = a[n]V[n], (7.17) where a[n] is the gain and V[n] is the unit-norm vector representing the direction of the beamforming vector. The optimization problem can be divided into two steps: first, we solve for the optimal direction V[n] as a function of −1 a[n] and then determine the optimal power allocation {a[n]}N n=0 under the total relay power constraint Pr . (i) Optimization of beamforming direction V[n] given a[n]: −1 Interestingly, for a given power allocation {a[n]}N n=0 , maximizing the −1 achievable sum rate with the channel directions {V[n]}N n=0 is equivalent to maximizing the SNR in (7.15) for each subcarrier individually. This results in the generalized eigenvalue problem, similar to the distributed beamforming design described in Chapter 4. Since the numerator Heff [n](Heff [n])H is of

286

7 Cooperation Relaying in OFDM and MIMO Systems

rank one, the beamforming direction can be solved in closed form as V[n] = U[n]/ U[n] ,

(7.18)

where 

(H1,d [n]β1 [n]Hs,1 [n])∗ (HL,d [n]βL [n]Hs,L [n])∗ U[n] = , . . . , |H1,d [n]β1 [n]|2 σr2 + σd2 /a[n] |HL,d [n]βL [n]|2 σr2 + σd2 /a[n]

T .

By substituting the corresponding G[n] and the definition of β [n] into (7.15), the SNR of the n-th subcarrier becomes SNR[n] =

L  =1

Ps a[n]|H,d [n]Hs, [n]|2 , a[n]|H,d [n]|2 σr2 + Ps |Hs, [n]|2 σd2 + σr2 σd2

(7.19)

which now only depends on the beamforming gain a[n]. −1 (ii) Optimization of beamforming gains {a[n]}N n=0 :

Given the optimal direction in (7.18) and the resulting G[n] and SNR[n], what remains to be solved in (7.16) is the power allocation problem:   N −1 L   Ps a[n]|H,d [n]Hs, [n]|2 maximize log 1 + a[n]|H,d [n]|2 σr2 + Ps |Hs, [n]|2 σd2 + σr2 σd2 n=0 =1

(7.20) subject to

N −1 

a[n] ≤ Pr and a[n] ≥ 0, ∀n.

n=0

Since the objective function is concave and the constraints are convex, the power allocation problem described above is a convex optimization problem that can be solved via standard convex optimization schemes such as the interior point method or the bisectional search dual method [3, 12].

Time-Domain Cooperative Beamforming When beamforming is performed in the frequency domain, DFT and IDFT computations must be performed at the relays and large number of feedback bits are required to characterize the N channel realizations. An alternative approach to beamforming in cooperative OFDM systems is the time-domain (TD) cooperative beamforming proposed in [12]. In this scheme, the signal received at each relay is passed through a time-domain cyclic beamforming filter (C-BFF) instead of having to perform DFT and IDFT. Let g = [g [0], . . . , g [μ − 1]]T be the time-domain C-BFF and let CM(g ) be the N × N circulant matrix where

7.3 Cooperative OFDM Systems with Multiple Relays

{CM(g )}i,j =

287

g [(i − j) mod N ], for (i − j) mod N < μ 0, otherwise.

Following the signal model in (7.12), the signal forwarded by relay is z = CM(g )y and the signal received at the destination is given by yd =

L 

CM(h,d )CM(g )CM(hs, )x + CM(h,d )CM(g )w + wd .

=1

At the destination, the same receiver processing is performed where the timedomain signal yd is first passed through the DFT filter to obtain a similar frequency-domain signal Yd [n] =

L 

H,d [n]G [n]Hs, [n]X[n] +

=1

L 

H,d [n]G [n]W [n] + Wd [n],

=1

for n = 0, . . . , N − 1. Notice that we omitted the scaling factors {β [n], ∀ , n} since each can be combined into their corresponding beamforming coefficients {G [n], ∀ , n}. Hence, the only difference between TD and FD beamforming is that, in TD beamforming, the frequency-domain beamforming coefficient G [n] is restricted by the dimensions of g since G [n] =

μ−1 

g [k]e−j2πkn/N ,

(7.21)

k=0

for n = 0, . . . , N − 1 and = 1, . . . , L. In general, when μ < N , the performance of TD beamforming will not perform as well as FD beamforming. However, when μ = N , TD beamforming can potentially achieve the same gains as FD beamforming. The advantage of TD beamforming is that it requires only μ variables to be fed back to the relays. This number may be considerably less than that required by FD beamforming, which is equal to the number of subcarriers N . Moreover, the computational complexity is also reduced since DFT and IDFT is not required at the relays. Similar to FD beamforming, the optimal beamforming coefficients can also be obtained by maximizing the achievable sum rate. Specifically, let T T g = [g1T , . . . , gL ] be the μL × 1 vector consisting of the C-BFFs of all relays and let F[n] = IL×L ⊗ f [n], where ⊗ is the Kronecker product and f [n] = [1, e−j2πn/N , . . . , e−j2π(μ−1)n/N ]T . In this case, the frequency-domain beamforming vector can be expressed as G[n] = F[n]T g and the optimization problem is formulated as follows:

288

7 Cooperation Relaying in OFDM and MIMO Systems

maximize

N −1 

* + log 1 + SNR[n]

(7.22)

n=0

subject to

N −1 

gT F[n]Υ[n]F[n]H g∗ ≤ Pr

n=0

where SNR[n] =

Ps gT F[n]Heff [n](Heff [n])H F[n]H g∗ ( ) , : r,d [n](H : r,d [n])H F[n]H σ 2 + (σ 2 /a)I g∗ gT F[n]H r d

+ * a = g 2 , and Υ[n] = diag |Hs,1 [n]|2 Ps + σr2 , . . . , |Hs,L [n]|2 Ps + σr2 . Recall that {β [n]}L =1 is incorporated into G[n] and, thus, the effective channel vector is now defined as Heff = [H1,d [n]Hs,1 [n], . . . , HL,d [n]Hs,L [n]]T . Unfortunately, the optimization problem described above is not a convex optimization problem since the objective function is not concave. Although it is difficult to guarantee globally optimal solutions in this case, standard optimization techniques such as the gradient descent (or ascent) algorithm can be used to obtain locally minimum solutions. Example: FD versus TD Cooperative Beamforming Let us consider the case similar to the example in the previous section, that is, N = 64, Δf = 312.5 KHz, and the sampling period ts = 50 ns. The ν-tap exponential delay multipath model with RMS delay spread of TRMS = 100 ns is adopted and E[||hs, ||] = E[||h,d ||2 ] = 1, for all . Here, we assume that the source transmission power Ps and the noise variances σr2 and σd2 are one. In Fig. 7.7, the sum rate for FD and TD cooperative beamforming is shown with C-BFFs of different length, say μ. As expected, the performance of TD beamforming is inferior to that of FD beamforming when μ < N . However, the performance loss is acceptable when μ is increased. Specifically, for a sum rate of 15 bits/sec/Hz, when μ is increased from 2 to 20, the performance loss compared with FD beamforming is decreased from approximately 2 dB to 0.5 dB. The reduced feed back overhead and computation complexity thus make TD beamforming a more flexible approach.

7.3.2 Selective Relaying for OFDM Multi-Relay Systems In the previous subsection, cooperative beamforming techniques are introduced for multi-relay OFDM systems. However, beamforming not only requires perfect CSI at the transmitters, but also requires perfect coordination and synchronization among relays. In view of these requirements, selective

7.3 Cooperative OFDM Systems with Multiple Relays

289

 )'%) 7'%)μ 7'%)μ 7'%)μ 7'%)μ 7'%)μ

6XP5DWHELWVVHF+]



    





















3UσGG%

Fig. 7.7 Comparison of the achievable sum rate between FD and TD cooperative beamforming schemes.

relaying (SR) is considered as a more practical alternative to achieving spatial and frequency diversity gains in cooperative OFDM systems. Specifically, two types of selective relaying schemes can be adopted in cooperative OFDM systems: the symbol-based SR and the subcarrier-based SR. In the symbolbased scheme, the data on all subcarriers of the same OFDM symbol are transmitted over the same relay while, in the subcarrier-based scheme, each subcarrier is allowed to select a different relay to forward its messages. Both DF and AF systems will be considered in the following.

Symbol-Based Selective Relaying Scheme In the symbol-based SR scheme, the relay with the maximum sum rate over all subcarriers will be selected to forward the OFDM symbol. In this case, the entire OFDM symbol will be forwarded by the same relay. Notice that, if the symbol is transmitted over relay , the achievable rate in the DF system is given by N −1  1 C = log(1 + min{SNRs, [n], SNR,d [n]}), 2 n=0

290

7 Cooperation Relaying in OFDM and MIMO Systems

where SNRs, [n] = Ps |Hs, [n]|2 /σr2 and SNR,d = Pr |H,d [n]|2 /σd2 are the SNRs on the n-th subcarrier of the s- and -d links. Moreover, the achievable rate in the AF system is given by C =

N −1  n=0

1 log(1 + g(SNRs, [n], SNR,d [n])), 2

where g(x, y) = xy/(x + y + 1). Intuitively, the relay selected to forward the OFDM symbol is the one that achieves the maximal transmission rate among all potential relays, i.e.,

∗ = arg max C . 

The symbol-based SR scheme is relatively easy to implement, but cooperative diversity is not fully exploited on all subcarriers. This can be improved upon with subcarrier-based SR and, additionally, with subcarrier matching.

Subcarrier-Based Selective Relaying Scheme The subcarrier-based SR scheme selects separately the best relay for each subcarrier and, thus, allows an OFDM symbol to be forwarded by multiple relays. In this case, the best relay for subcarrier n will be given by

∗n = arg max C [n], 

where C [n] = 12 log(1 + min{SNRs, [n], SNR,d [n]}) is the rate achievable by transmitting on the n-th subcarrier of relay in the DF system and C [n] = 1 2 log(1+g(SNRs, [n], SNR,d [n])) is that in the AF system. The achievable sum rate of the system is given by Csub =

N −1  n=0

max C [n], 

which is clearly greater  −1than the sum rate of the symbol-based SR scheme, i.e. Csymb = max N n=0 C [n]. However, the subcarrier-based SR scheme requires extra coordination and synchronization among relays since an OFDM symbol is spread among different relays. Fortunately, OFDM systems are inherently more robust to timing errors and, thus, makes the scheme realizable.

OFDM Selective Relaying with Subcarrier Matching In the previous SR policies, the data on each subcarrier is forwarded on the same subcarrier regardless of the relay selection. In this case, each subcarrier’s

7.3 Cooperative OFDM Systems with Multiple Relays

291

data can select among L equivalent s-r-d channels to transmit its data. Certainly, this can be improved upon by imposing optimal subcarrier matching as in Section 7.2.2. Different from the pair-wise cooperative system, where the subcarrier matching problem is intractable, the optimal subcarrier matching for this case can be shown to be the ordered subcarrier pairing (OSP) scheme. Let π : {0, . . . , N − 1} → {0, . . . , N − 1} be the subcarrier matching function where subcarrier n on the r-d link is used to relay the message originally transmitted on subcarrier π(n) of the s-r link. Specifically, in the OSP scheme, the subcarrier with the best channel on the s-r link is matched to the subcarrier with the best channel on the r-d link. For systems with multiple relays, the effective channel of subcarrier n on the s-r and the r-d links is defined as SNRs,r [n]  max SNRs, [n] and SNRr,d [n]  max SNR,d [n], 



respectively. Let φs,r : {0, . . . , N − 1} → {0, . . . , N − 1} and φr,d : {0, . . . , N − 1} → {0, . . . , N − 1} be the order-mappings such that SNRs,r [φs,r (0)] ≤ SNRs,r [φs,r (1)] ≤ . . . ≤ SNRs,r [φs,r (N − 1)] and SNRr,d [φr,d (0)] ≤ SNRr,d [φr,d (1)] ≤ . . . ≤ SNRr,d [φr,d (N − 1)]. Then, the OSP scheme can be represented by the subcarrier matching function πOSP defined by πOSP (φr,d (n)) = φs,r (n) for n = 0, . . . , N − 1. To show that OSP is optimal, consider an instance where SNRs,r [n1 ] < SNRs,r [n2 ] and SNRr,d [m1 ] > SNRr,d [m2 ]. In this case, one can show that     SNRs,r [n2 ]SNRr,d [m2 ] SNRs,r [n1 ]SNRr,d [m1 ] +log 1+ log 1+ 1+SNRs,r [n1 ]+SNRr,d [m1 ] 1+SNRs,r [n2 ]+SNRr,d [m2 ]     SNRs,r [n2 ]SNRr,d [m1 ] SNRs,r [n1 ]SNRr,d [m2 ] +log 1+ < log 1+ 1+SNRs,r [n1 ]+SNRr,d [m2 ] 1+SNRs,r [n2 ]+SNRr,d [m1 ] in the AF case and * + * + log 1+min{SNRs,r [n1 ], SNRr,d [m1 ]} +log 1+min{SNRs,r [n2 ], SNRr,d [m2 ]} * + * + < log 1+min{SNRs,r [n1 ], SNRr,d [m2 ]} +log 1+min{SNRs,r [n2 ], SNRr,d [m1 ]} in the DF case. Therefore, whenever the subcarriers are not matched according to πOSP , there must exist n1 , n2 , m1 , m2 such that π(m1 ) = n1 , π(m2 ) = n2 and that SNRs,r [n1 ] < SNRs,r [n2 ] and SNRr,d [m1 ] > SNRr,d [m2 ]. Consequently, one can always achieve a larger sum rate by switching the subcarriers matched to m1 and m2 , i.e., by taking π(m2 ) = n1 , π(m1 ) = n2 . This can be done continuously until no such instance exists, in which case, the function π will be equal to πOSP and the maximum sum rate will be

292

7 Cooperation Relaying in OFDM and MIMO Systems

achieved. Following the derivations in [9], we can also prove the optimality of the OSP scheme even when power allocation is employed across subcarriers.

7.4 Distributed Space-Frequency Codes

Fig. 7.8 System model of the multi-relay network with multipath fading.

In the previous section, we have shown how beamforming and selective relaying can be extended to multi-carrier systems and how advanced designs can help exploit degrees of freedom in the frequency domain. Certainly, distributed space-time coding schemes can also be employed in cooperative OFDM systems as well, by transmitting a separate DSTC on each subcarrier. However, this approach does not exploit the frequency diversity gains available in OFDM systems. An important class of techniques that achieves this task is the so-called distributed space-frequency codes (DSFC) and will be discussed in this section. Consider a cooperative OFDM system that consists of a source transmitting to a destination with the help of L relay nodes, as illustrated in Fig. 7.8. Similar to previous schemes, cooperation is achieved in two phases: the broadcast transmission by the source and the cooperative transmission by the relays. We assume that the destination is beyond the range of the source and, thus, receives signals only from the relays. In Phase I, the source first transmits the sequence of symbols Xs = [Xs [0], Xs [1], · · · , Xs [M − 1]]T to the relays. Upon receiving the signal, each relay, say relay , then generates a codeword X = [X [0], . . . , X [N − 1]]T based on the specific relaying or space-frequency coding scheme, where N is the number of subcarriers. Notice that the number of source symbols M needs not be equal to N . (In DF-based transmission scheme, the source may not even need to transmit using OFDM transmissions.) The space-frequency block code (SFBC) Xr = [X1 , X2 , · · · , XL ]

(7.23)

7.4 Distributed Space-Frequency Codes

293

is then transmitted by the L relays to the destination. The signal received at the destination is given by L  Yd [n] = P H,d [n]X [n] + Wd [n] for n = 0, . . . , N − 1,

(7.24)

=1

where H,d [n] is the -d channel frequency response on the n-th subcarrier, P is the transmission power of relay and Wd [n] ∼ CN (0, σd2 ) is the AWGN on the n-th subcarrier. Here, we assume that the length on the s- and -d channels are identical, i.e., νs, = ν,d = ν, ∀ . The results can be easily generalized to the case with non-identical channel lengths, e.g., in [11, 17]. In the DF-based transmission scheme, relays will attempt to decode the message from the source and will only participate in the cooperative transmission in Phase II if it is able to correctly decode it. Whether or not the message is correctly decoded is determined using, e.g., cyclic redundancy check (CRC) codes. Here, we assume that all relays that correctly decode the message will relay with power Pr (i.e. P = Pr ) and remain silent (i.e. P = 0), otherwise. In the AF-based transmission scheme, all relays will forward the received signal since no decoding is performed. Hence, we have P = Pr for all . It is worthwhile to mention that, in contrast to the schemes presented in previous sections, DSFC does not require knowledge of the -d channels at the relays.

7.4.1 Decode-and-Forward Space-Frequency Codes Let us first consider the case where all L relays are able to correctly decode the source’s message, i.e., P = Pr , ∀ . Let h,d = [h,d [0], . . . , h,d [ν − 1]]T be the discrete baseband channel vector on the -d link and let H,d = [H,d [0], H,d [1], · · · , H,d [N − 1]]T = Fν h,d ,

(7.25)

where Fν = [f (0) , f (1) , · · · , f (ν−1) ] is an N × ν matrix that consists of the first ν columns of the DFT ma(N −1)v v trix with f (v) = √1N [1, e−j2π N , · · · , e−j2π N ]T , for v = 0, . . . , ν − 1. By (7.24) and (7.25), the received signal vector at the destination, i.e., Yd = [Yd [0], Yd [1], · · · , Yd [N − 1]]T , can be expressed as Yd =

L -  Pr X  H,d + Wd =1

L ( ) -  X  f (0) , X  f (1) , . . . , X  f (ν−1) h,d + Wd (7.26) = Pr =1

294

7 Cooperation Relaying in OFDM and MIMO Systems

where  is the Hadamard product, i.e., the element-wise product defined by [A B]i,j = [A]i,j [B]i,j , for any two matrices A, B with the same dimension. Let us define F(v)  [f (v) , f (v) , · · · , f (v) ] as an N × L matrix that consists of f (v) on all L columns and define : r,d = [h1,d [0], h2,d [0], . . . , hL,d [0], h1,d [1], . . . , h1,d [ν − 1], . . . , hL,d[ν − 1]]T as h the collection of channel coefficients of all fading paths between the relays and destination. Then, the received vector can be rewritten as -  : r,d + Wd (7.27) Yd = Pr F(0)  Xr , F(1)  Xr · · · F(ν−1)  Xr h : r,d + Wd , (7.28) ≡ Pr Cr h  where Cr = F(0)  Xr , F(1)  Xr · · · F(ν−1)  Xr is an N × Lν matrix that depends on the space-frequency codeword Xr . Given the received vector Yd , the SFC Cr (or, equivalently Xr ) is detected at the destination using ML decoding. The resulting pairwise error probability (PEP) [11] can be upper bounded by  ˆ r) ≤ Pr(Xr → X

Pr σd2

−κ 

2κ − 1 κ

  κ

−1 λi

,

(7.29)

i=1

where κ is the rank and {λi }κi=1 is the set of nonzero eigenvalues of (Cr − ˆ r )H , and Rh is the Lν × Lν correlation matrix of the channel ˆ r )Rh (Cr − C C : r,d h : : H ]. By assuming that the channel coefficients vector hr,d , i.e., Rh = E[h r,d {h,d [v], ∀v, } are zero-mean and independent, the correlation matrix Rh will be diagonal. The average error probability (and, thus, the diversity gain) of a space-frequency code is dominated by the codeword pair with largest PEP. Hence, similar to the STC code design in conventional MIMO systems, the design criteria of DSFC Xr are also given by [2, 11]: ˆ r )Rh (Cr − C ˆ r )H 1. Rank Criterion: The minimum rank of the matrix (Cr − C ˆ ˆ over all codeword pairs in {(Xr , Xr ) : Xr = Xr } should be as large as possible. >κ 2. Determinant Criterion: The minimum value of the product i=1 λi (i.e., ˆ r )Rh (Cr − C ˆ r )H ) over all the product of nonzero eigenvalues of (Cr − C ˆ r ) : Xr = X ˆ r } should be as large as possible. codeword pairs in {(Xr , X As observed from the PEP in (7.29), the minimum rank κ over all pairs of different codewords determines the diversity order, while the minimum product of nonzero eigenvalues leads to the coding gain. At high SNR, the rank criterion dominates the PEP and should be given more emphasis in the code design. After finding a class of SFCs that achieves the rank criterion, we may then choose the SFC that yields the largest product of nonzero eigenvalues ˆ r )Rh (Cr − C ˆ r )H is full rank over among this class of codes. When (Cr − C all distinct codeword pairs, the maximum achievable diversity is given by

7.4 Distributed Space-Frequency Codes

295

d = min (N, Lν) . When N > Lν, the maximum diversity order is equal to Lν, which is the total number of independent fading paths from the relays to the destination. When channel lengthes of all relay-destination links are different, the result of maximum achievable diversity can be easily generalized to   L  d = min N, ν,d , =1

where ν,d is the length of the -d channel. In this case, the channel length ν can be set as max ν,d when constructing DSFC codes. Li-Zhang-Xia DSFC Code Construction [11] A DSFC code construction that achieves full diversity has been proposed by Li, Zhang, and Xia in [10]. Suppose that the relays receive M = N L symbols from the source in Phase I. Assume that each symbol belongs to an arbitrary complex symbol constellation, such as QAM or PAM, and that the number of subcarriers is N = ηJ, where η  2log2 L+log2 ν and J is some positive integer. The received sequence Xs is divided into J sub-blocks so that each sub-block consists of ηL symbols. That is, let Xs = [XTs1 , . . . , XTsJ ]T , where Xsj = [Xs [(j − 1)ηL + 1], . . . , Xs [jηL]]T is the j-th sub-block of length ηL. Each sub-block of source symbols, say Xsj , is encoded individually into the space-frequency matrix Bj with dimension η × L. The DSFC codeword is then given by ⎡ ⎤ B1 ⎢ B2 ⎥ ⎥ ⎢ (7.30) Xr = ⎢ . ⎥ . ⎣ .. ⎦ BJ Define ηL  2log2 L and ην  2log2 ν such that η = ηL ην . The sub-block of codewords Bj (for j = 1, 2, · · · , J) is constructed by taking ⎡ ⎤ A1 ⎢ A2 ⎥ ⎢ ⎥ (7.31) Bj = ⎢ . ⎥ , ⎣ .. ⎦ Aην where {Ai , for i = 1, 2, · · · , ην } are ηL × L (ηL ≥ L) matrices constructed by the j-th sub-block of source symbols, i.e., Xsj . Specifically, let us further divide Xsj into ηL sub-blocks such that Xsj = [XTsj1 , . . . , XTsjηL ]T , where Xsjm is the m-th sub-block of Xsj with ην L elements. Given Xsjm , we can construct the so-called m-th layer symbol vector

296

7 Cooperation Relaying in OFDM and MIMO Systems

a(m) = [a(m) [1], . . . , a(m) [ην L]]T = ΘXsjm ,

(7.32)

for m = 1, 2, · · · , ηL , where Θ is an ην L × ην L unitary matrix and will discussed later. The matrix Ai (i = 1, 2, · · · , ην ) is given by ⎡ a(1) [ki +1] φa(2) [ki +1] · · · φL−1 a(L) [ki +1] ⎢ φηL−1 a(ηL ) [ki +αL +1] a(1) [ki +2] · · · φL−2 a(L−1) [ki +2] ⎢ Ai = ⎢ .. .. .. .. ⎣ . . . .

be ⎤ ⎥ ⎥ ⎥, ⎦

φ2 a(3) [ki +L] · · · φ(1−αL )L a(1+(1−αL)L) [ki +L] (7.33) where ki = (i − 1)L, αL = L/ηL  ∈ {0, 1}, and φ is the phase rotation parameter. The elements of Ai follow the rules: (1) the symbols in the m-th layer are placed circularly, (2) the power of φ multiplied by the m-th layer symbols equals to m−1, (3) the entry a(m) [ki +j] is the j-th encoded symbol of the m-th layer placed in Ai from top to down. For example, for L = 3, we have ηL = 4 and ⎡ (1) ⎤ a [ki +1] φa(2) [ki +1] φ2 a(3) [ki +1] ⎢ φ3 a(4) [ki +1] a(1) [ki +2] φa(2) [ki +2] ⎥ ⎥ Ai = ⎢ ⎣ φ2 a(3) [ki +2] φ3 a(4) [ki +2] a(1) [ki +3] ⎦ , φa(2) [ki +L]

φa(2) [ki +3] φ2 a(3) [ki +3] φ3 a(4) [ki +3] or, for L = 4, ⎡

⎤ a(1) [ki +1] φa(2) [ki +1] φ2 a(3) [ki +1] φ3 a(4) [ki +1] ⎢ φ3 a(4) [ki +2] a(1) [ki +2] φa(2) [ki +2] φ2 a(3) [ki +2] ⎥ ⎥ Ai = ⎢ ⎣ φ2 a(3) [ki +3] φ3 a(4) [ki +3] a(1) [ki +3] φa(2) [ki +3] ⎦ . φa(2) [ki +4] φ2 a(3) [ki +4] φ3 a(4) [ki +4] a(1) [ki +4]

The unitary matrix Θ and the phase rotation parameter φ must be appropriately chosen in order to achieve full diversity [10]. One choice is to set the unitary matrix Θ as the first principal ην L × ην L submatrix of the matrix  : = FH diag(1, ϕ, · · · , ϕL−1 ), Θ  L

(7.34)

:×L : DFT matrix. : = 2log2 (ην L) , ϕ = ejπ/2L , and F  is the L where L L Meanwhile, the phase rotation parameter is set as φ = θ1/ηL ,

(7.35)

where θ is an algebraic element with degree at least ην ηL over the extension field of the field Q of rational numbers, which contains all entries of Θ, the signal alphabet of the symbol constellation, and e−j2π/N . This construction of the DSFC has been shown to achieve full diversity of Lν. Details of the

7.4 Distributed Space-Frequency Codes

297

Fig. 7.9 Symbol error rate comparison for distributed space-frequency code in (7.36) and the Alamouti coded OFDM scheme for the case with L = 2 relays, channel length ν = 2, c and single antenna equipped at the destination. (From Li, Zhang, and Xia. 2009 IEEE.)

proof can be found in [11]. Example: Consider the example, as given in [11], where the system consists of L = 2 relays and channel length is equal to ν = 2 with power delay profile [1/2, 1/2]. As a result, we have ηL = 2 and ην = 2. The matrix Bj can be constructed by ⎡ (1) ⎤ a [1] φa(2) [1] ⎢ φa(2) [2] a(1) [2] ⎥ ⎥ Bj = ⎢ (7.36) ⎣ a(1) [3] φa(2) [3] ⎦ , φa(2) [4] a(1) [4] where

⎡

a(m)

⎤ a(m) [1] ⎢ a(m) [2] ⎥ ⎥ =⎢ ⎣ a(m) [3] ⎦ = ΘXsjm , a(m) [4]

for m = 1, 2,

(7.37)

and the elements in Xsjm are QPSK data symbols. Assume that the length of the OFDM symbol is 64, then θ can be set as θ = e−jπ/8 , and the unitary matrix Θ can be constructed by

298

7 Cooperation Relaying in OFDM and MIMO Systems

Fig. 7.10 Symbol error rate comparison for distributed space-frequency code in (7.36) and the Alamouti coded OFDM scheme for the case with L = 2 relays, channel length c ν = 2, and two antennas equipped at the destination. (From Li, Zhang, and Xia. 2009 IEEE.)

⎤⎡ 1 1 1 1 1 0 0 0 π 3π j2 jπ j 2 ⎥ ⎢ 0 ej π 8 1⎢ 0 0 1 e e e ⎥⎢ Θ= ⎢ jπ 2 ⎣ 1 ejπ ej2π ej3π ⎦ ⎣ 0 0 e 4 0 3π 3π 9π 0 0 0 ej 8 1 ej 2 ej3π ej 2 ⎡

⎤ ⎥ ⎥. ⎦

(7.38)

Let us first consider a case where the destination is equipped with one antenna, and the phase parameter in (7.36) is set as φ = 0, i.e., only one layer is used in the DSFC and the transmission rate is R = 2 bits/sec/Hz. In Fig.7.9, the corresponding SER performance is compared with the STCOFDM scheme in terms of total transmit SNR ρ = LPr /σd2 . In STC-OFDM scheme, the data symbols, modulated by QPSK, are space-time coded by Alamouti scheme first, and then transmitted with OFDM. It is observed from Fig.7.9 that the DSFC achieve higher diversity order contributed by multipath propagation. When the destination is equipped with multiple antennas, the signal received at each antenna can be further combined to attain receiver diversity. In Fig. 7.10, consider that the destination has two antennas, and the phase parameter is chosen as φ = ejπ/64 . Since two layers of symbols are adopted, the transmission rate is 2 symbols per channel use, i.e., R = 4 bits/sec/Hz. The SER performance of the above-mentioned DSFC is compared with the STC-OFDM scheme in Fig. 7.10, where the 16QAM is used in STC-OFDM scheme for fairness of transmission rate. In Fig. 7.10, one can observe that

7.4 Distributed Space-Frequency Codes

299

the Li-Zhang-Xia DSFC achieves both cooperative and multipath diversity. Seddik-Liu DSFC Code Construction [17] Another DF-based DSFC scheme was proposed by Seddik and Liu [17]. In contrast to the previous scheme, the Seddik-Liu DSFC scheme does not require the number of subcarriers N to be integer multiples of η. Specifically, consider a cooperative OFDM system where the number of subcarrier N is much greater than Lν. In the Seddik-Liu DSFC scheme, the DSFC codewords transmitted by the relays is given by ⎤ ⎡ U1 ⎥ ⎢ U2 ⎥ ⎢ ⎥ ⎢ . .. Xr = ⎢ (7.39) ⎥, ⎥ ⎢ ⎦ ⎣ UQ 0(N −QLν)×L where Uq is the q-th Lν × L submatrix of the codeword and Q = N/(Lν) is the number of nonzero sub-block in the codeword. This implies that only the first QLν subcarriers will be utilized by the relays if N is not an integer multiple of Lν. Each sub-block, say sub-block q, has the block-diagonal structure given by ⎡ ⎤ uq,1 0ν×1 · · · 0ν×1 ⎢ 0ν×1 uq,2 · · · 0ν×1 ⎥ ⎢ ⎥ Uq = ⎢ . (7.40) .. . . .. ⎥ , . ⎣ . . . ⎦ . 0ν×1 0ν×1 · · · uq,L where each uq, is a ν × 1 vector. The DSFC is designed such that any two distinct codewords differ in at least one sub-block. That is, for two distinct ˆ r , there exists at least one index q0 such that Uq0 = U ˆ q0 . codewords Xr and X The construction of codewords {uq, , ∀ } is described as follows. Let M = QLν and Xs  [Xs1 , Xs2 , · · · , XsQ ]T be the vector of source symbols, where Xsq is the q-th sub-block with length Lν. For q = 1, 2, · · · , Q, the codeword vector Uq  [uq,1 , uq,2 , · · · , uq,L ] is constructed from a linear transform of Xsq , i.e., Uq = XTsq M, (7.41) where M is a linear transform matrix. The matrix M can be obtained from Hadamard transforms or Vandermonde matrices, and both are able to achieve full diversity. However, adopting Vandermonde matrices yields larger miniˆ r )Rh (Cr − C ˆ r )H [17,19]. The mum product of nonzero eigenvalues of (Cr − C matrix M using Vandermonde matrices is expressed by

300

7 Cooperation Relaying in OFDM and MIMO Systems

1 M = √ T(θ1 , θ2 , · · · , θLν ) Lν ⎡ ⎤ 1 1 ··· 1 θ2 · · · θLν ⎥ 1 ⎢ ⎢ θ1 ⎥  √ ⎢ .. .. ⎥ , .. .. . Lν ⎣ . . ⎦ . Lν−1 Lν−1 Lν−1 θ1 θ2 · · · θLν

(7.42)

where θ1 , θ2 , · · · , θLν are phase parameters. When Lν = 2m with any positive integer m and QAM modulation, the optimum transform is obtained from a Vandermonde matrix in (7.42) where   (4k − 3)π θk = exp j , for k = 1, 2, · · · , Lν. 2Lν For more details of the design of the codewords Uq , the interested readers are referred to [19]. To evaluate the PEP in (7.29), let us consider the worst case scenario ˆ r differ in only one sub-block. where the two different codewords Xr and X In this case, we have T T ˆ r = [0T ˆT T Xr − X (q0 −1)Lν×L , Uq0 − Uq0 , 0(N −q0 Lν)×L ]

and

( ) ˆ r ) = F(0) (Xr − X ˆ r ), F(1) (Xr − X ˆ r ), · · · , F(ν−1) (Xr − X ˆ r) (Cr − C ⎡ ⎤ 0(q0 −1)Lν×L 0(q0 −1)Lν×L · · · 0(q0 −1)Lν×L (1) (ν−1) ˆ ˆ ˆ q0)⎦ , = ⎣F(0) (Uq0 − U q0 (Uq0 − Uq0) Fq0 (Uq0 − Uq0) · · · Fq0 0(N −q0 Lν)×L 0(N −q0 Lν)ν×L · · · 0(N −q0 Lν)ν×L (v)

where Fq0 = [0Lν×(q0 −1)Lν , ILν×Lν , 0Lν×(N −q0 Lν) ]F(v) . Thus, the rank κ and the eigenvalues {λi } required to evaluate the PEP corresponds to the matrix ν−1 ( ) )H ( (v) ˆ ˆ Fq(v) F (U − U ) R (U − U ) , (v) q0 q0 q0 q0 h q0 0 v=0

where Rh(v) is the correlation matrix of [h1,d [v], h2,d [v], · · · , hL,d [v]]T . When decoding error is taken into consideration, the relays that are not able to correctly decode the source message (according to the CRC) will not be transmitting in Phase II. Let Ir = diag(χ1 , χ2 , · · · , χL ) be an L × L diagonal matrix where χ = 1 if relay is able to correctly decode and χ = 0, otherwise. For a given decoding status Ir , the PEP can be expressed similarly by (7.29), where κ and {λi } are the rank and eigenvalues of the matrix

7.4 Distributed Space-Frequency Codes

301

ν−1 (

( )H ) ˆ q0) . ˆ q0) Ir Rh(v) Ir Fq(v)(Uq0 − U Fq(v) − U (U q 0 0 0

(7.43)

v=0

By assuming that ε is the error probability at relay , the PEP can be obtained by ˆ r) ≤ Pr(Xr → X

 

(1−ε)

Ir :χ=1



 ε

:χ=0

Pr σd2

 κ −1 −κ  2κ − 1  λi , (7.44) κ i=1

where κ and {λi }κi=1 are the rank and the eigenvalues of the matrix in (7.43) and depend on the decoding status Ir . Notice that one can also employ appropriate transmission schemes at the source to exploit multipath diversity gains between the source and the relays (c.f. [17]). Assume that Ps /σr2 = Pr /σd2 = SNR. In this case, the symbol error rate at relay , i.e., SER , can be bounded as SER ≤ c × SNR−ν , where c is a constant that depends on . In fact, it has been shown in [17] that, with such transmission schemes at the source and with the aforementioned DSFC at the relays, full diversity Lν can be achieved even with decoding errors at the relays.

7.4.2 Amplify-and-Forward Space-Frequency Codes The Seddik-Liu DSFC design in (7.39) and (7.40) can be generalized to the AF system as shown in [16, 17]. Similarly, let us assume that the number of subcarriers N is much greater than Lν. Different from the DSFC in DF system where the DSFC are applied at relays, the source symbols are encoded both in space and frequency domain and transmitted directly from the source. Suppose that, in Phase I, the source transmits over N subcarriers the codeword Xs = [Xs [0], Xs [1], · · · , Xs [N − 1]]T T  T = V1T , V2T , · · · , VQ , 0(N −QLν)×1 ,

(7.45)

where Q = N/(Lν) and Vq is the q-th sub-block of the codeword with length Lν. Each sub-block, say Vq , is further partitioned as T T T T Vq = [vq,1 , vq,2 , · · · , vq,L ] ,

q = 1, 2, · · · , Q,

where the length-ν sub-block vq, is forwarded only by relay . The construction of codewords {vq, } comes from linear transform of the source symbols,

302

7 Cooperation Relaying in OFDM and MIMO Systems

as shown in (7.41), and Vandermonde matrices in (7.42) can be chosen to achieve full diversity order. During Phase II, at relay , the signals received on the subcarriers corresponding to-the sub-blocks v1, , v2, , · · · , vQ, are first scaled by the factor β [n] = 1/ Ps |Hs, [n]|2 + σr2 and then forwarded to the destination with power Pr on each subcarrier, and zeroes are sent over the other subcarriers of relay . At the destination, the signal received on the n-th subcarrier is given by ' Ps Pr Yd [n] = H [n]H[n],d [n]Xs [n] Ps |Hs,[n] [n]|2 + σr2 s,[n] ' Pr H[n],d [n]W[n] [n] + Wd [n] (7.46) + Ps |Hs,[n] [n]|2 + σr2 ' Ps Pr Dd [n] (7.47) Hs,[n] [n]H[n],d [n]Xs [n] + W  Ps |Hs,[n] [n]|2 + σr2 where [n] ∈ {1, 2, · · · , L} is the index of the relay node that forwards the Dd [n] data associated with subcarrier n, i.e., [n] = (n mod Lν)/ ν+1, and W is the equivalent noise at the destination with variance 2 2 2 σW  [n] = σd + σr

Pr |H[n],d [n]|2 . Ps |Hs,[n] [n]|2 + σr2

(7.48)

By employing ML detection, the destination obtains the estimate  2 '   1 P P   s r ˆ s = arg min Y [n]− H [n]H [n]X [n] X   s [n],d 2 [n]  d 2 +σ 2 s,[n]  σ P |H [n]| s Xs s,[n] r  n=1 W (7.49) Given the channel realizations H = {Hs, [n], H,d [n], ∀n, }, the PEP can upper bounded, as shown in [16, 17], by ) (  s ,H)−log p(Yd |Xs ,H)] : s |H) = E eμ[log p(Yd |X Pr(Xs → X , (7.50) N 

where μ is a constant selected to minimize the upper bound. The PEP can be further averaged over the channel statistics, but the integral becomes difficult to evaluate for ν > 2. However, for ν = 1, 2, the average PEP can be approximated as follows. : s , there exists at Assume that for any pair of distinct codewords Xs and X : q0 , least one sub-block, say q0 , in which the two codewords differ, i.e., Vq0 = V : : where Vq0 is the corresponding sub-block in Xs . To obtain an upper bound for the average PEP, we consider the worst case scenario where the two codewords differ in only one sub-block. Assume that Ps /σr2 = Pr /σd2 = SNR

7.4 Distributed Space-Frequency Codes

303

and Hs, [n], H,n [n] are zero-mean complex Gaussian random variables with unit variance, for all , n. It has been shown in [16, 17] that the average PEP for ν = 1 at high SNR can be approximated by : s)  Pr(Xs → X



SNR 16

−L   L

−1 : q [n]|2 |Vq0 [n] − V 0

,

(7.51)

n=1

where Vq0 [n] is the n-th component in the vector Vq0 . For the case with ν = 2, we can consider the case where the source-relay channels of different subcarriers of the same relay node are correlated and so are the relaydestination channels. The correlation between the channel coefficients of any two subcarriers are assumed to be the same, and we let ρ be the correlation coefficients of the pairs of random variables (|Hs, [n1 ]|2 , |Hs, [n2 ]|2 ) and (|H,d [n1 ]|2 , |H,d [n2 ]|2 ), for any subcarriers n1 = n2 and = 1, 2, · · · , L. In this case, the average PEP can be upper bounded by −1 −2L    2L SNR(1 − ρ) 2 : : Pr(Xs → Xs )  |Vq0 [n] − Vq0 [n]| . 16 n=1

(7.52)

One can observe from (7.51) and (7.52) that a DSFC that can guarantee difference in at least one sub-block achieves full diversity of Lν for the cases with ν = 1 and ν = 2. Example: Comparison of DF and AF DSFC Schemes [17] In Fig. 7.11 and Fig.7.12, we compare the symbol error probabilities of the DF and AF DSFC schemes under the Seddik-Liu design for ν = 2 and ν = 4, respectively. In these experiments, the number of relays is L = 2, the number of subcarriers is N = 128, and the total system bandwidth is W = 1 MHz. Different paths in each channel realizations are separated by 5 μs, and the data symbols are modulated by BPSK. Here, we assume that Ps = Pr and σr2 = σd2 , and define SNR = (Ps + Pr )/σr2 . The DSFC of the DF-based scheme is described in (7.39) and that of the AF-based scheme is described in (7.45). The power delay profile is given identically by E[|hs, [v]|2 ] = σ12 and E[|h,d[v]|2 ] = σ22 , for all v = 0, · · · , ν − 1 and = 1, 2. In the figures, the solid lines represent the case where the relay is located in the middle of the source and destination and, thus, σ12 = σ22 = 1. The dashed lines represent the case where the relay is close to the destination and, thus, channel statistics are given by (σ12 , σ22 ) = (1, 10). The dotted line is the opposite case where the relay is close to the source node and, thus, (σ12 , σ22 ) = (10, 1). From the simulation results, one can observe that DF outperforms AF in all cases by 2 − 4 dB. Moreover, even though the diversity order of the AF-based scheme with ν = 4 is difficult to attain analytically, the simulation results show that the AF-based DSFC achieves the same diversity order as the DF-based scheme, where the latter has been shown to achieve full diversity.

304

7 Cooperation Relaying in OFDM and MIMO Systems BPSK modulation, two relays, L=2, delays=[0, 5μsec]

0

10

−1

10

−2

SER

10

−3

10

AAF $) (all channel variances are ones) DAF ') (all channel variances are ones) AAF $) (relays close to destination) DAF ') (relays close to destination) AAF $) (relays close to source) DAF ') (relays close to source)

−4

10

−5

10

0

5

10

15

20

SNR (dB)

Fig. 7.11 Symbol error probabilities for distributed space-frequency code in (7.36) and the Alamouti coded OFDM scheme for the case with L = 2 relays and channel length c ν = 2. (From Seddik and Liu with modified labels. 2008 IEEE.) BPSK modulation, two relays, L=4

0

10

−1

10

−2

10

−3

SER

10

−4

10

−5

AAF $) (all channel variances are ones) DAF ') (all channel variances are ones) AAF $) (relays close to destination) DAF ') (relays close to destination) AAF $) (relays close to source) DAF ') (relays close to source)

10

−6

10

−7

10

0

5

10

15

20

SNR (dB)

Fig. 7.12 Symbol error probabilities for distributed space-frequency code in (7.36) and the Alamouti coded OFDM scheme for the case with L = 2 relays and channel length c ν = 4. (From Seddik and Liu with modified labels. 2008 IEEE.)

7.5 Cooperation with MIMO Relays

305

7.5 Cooperation with MIMO Relays One of the main advantages of cooperative communications is the ability to achieve spatial diversity gains without employing multiple antennas on each terminal. However, if the users can be equipped with multiple antennas, more spatial degrees of freedom can be exploited and more design flexibility would be available since the antennas are not completely distributed. In fact, similar to conventional MIMO systems (c.f. Chapter 2), precoders and decoders can be designed to decompose the MIMO channel into multiple independent eigen-channels and appropriate power and rate allocation policies can be employed to exploit the available spatial dimensions. Consider a cooperative system that consists of a source, a relay, and a destination, each equipped with Ms , Mr , and Md antennas, respectively, as shown in Fig.7.13. Similarly, we assume that the relay is half-duplex and the cooperation takes on two phases of transmission. In Phase I, the source transmits a symbol vector xs = [xs [1], . . . , xs [Ms ]]T to the relay and the destination, where xs [m] is the symbol transmitted on the m-th antenna. The signals received at the relay and the destination are given by yr = Ps Hs,r xs + wr and (1)

yd =

(1) Ps Hs,d xs + wd ,

respectively, where Ps is the transmission power of the source, Hs,r , Hs,d are Mr × Ms and Md × Ms channel matrices of the s-r and the s-d links, (1) and wr ∼ CN (0Mr , σr2 IMr ×Mr ), wd ∼ CN (0Md , σd2 IMd ×Md ) are the AWGN at the relay and the destination, respectively. The ( , k)-th element of Hs,r is the channel coefficient between the k-th antenna of the source and the

-th antenna of the relay, and the (i, k)-th element of Hs,d is the channel coefficient between the k-th antenna at the source and the i-th antenna at the destination. Moreover, assume that the source symbol vector is zero mean circularly symmetric complex Gaussian and the covariance matrix of the 1 source symbol vector is given by Rs  E[xs xH s ] = Ms IMs ×Ms . In Phase II, the relay generates an Mr ×1 symbol vector xr with E[xH r xr ] = 1 according to the specific cooperation scheme and forwards the signal to the destination with power Pr . The signal received at the destination in Phase II is given by (2) (2) yd = Pr Hr,d xr + wd , (7.53) where Hr,d is an Md × Mr channel matrix with the (i, )-th element being the channel coefficient between the -th antenna at the relay and the i-th antenna (2) at the destination, and wd ∼ CN (0Md , σd2 IMd ×Md ) is the AWGN at the destination. Note that, in DF systems, the MIMO relay link can be treated

306

7 Cooperation Relaying in OFDM and MIMO Systems

Fig. 7.13 Model of a cooperative system with multiple antennas at source, relay, and destination.

as the concatenation of two point-to-point MIMO channels and conventional MIMO precoder designs can be employed separately on each link to exploit the available spatial dimensions. The interaction between source and relay also follows closely that of single-antenna cooperative systems [5, 23]. In the following, we shall focus on the AF relaying scheme where the signal vector xr is a linear transformation of yr . For simplicity, we denote an M -by-M identity matrix by IM from now on. Consider the AF-based MIMO relay system where the relay employs a linear precoder F on the received signal vector. Therefore, the signal transmitted by the relay is given by xr = Fyr , (7.54) where F is an Mr × Mr precoding matrix. With the linear precoder F, the signal received by the destination in Phase II can be written as (2) (2) yd = Ps Pr Hr,d FHs,r xs + Pr Hr,d Fwr + wd (2) :d , = Ps Pr Hr,d FHs,r xs + w (2)

:d where w matrix

=

√ (2) Pr Hr,d Fwr + wd is the effective noise with covariance

) ( (2) (2)H 2 :d :d w Rw = Pr σr2 Hr,d FFH HH   E w r,d + σd IMd .

(7.55)

Given the instantaneous CSI at the relays, the precoding matrix F can be designed to maximize the channel capacity subject to the total power constraint among the relays, i.e.,     * + Ps H H 2 H tr E[xr xr ] = tr F ≤ 1. (7.56) Hs,r Hs,r + σr IMr F Ms

7.5 Cooperation with MIMO Relays

307

Without Source-to-Destination Link Let us first consider the case where the destination is located beyond the transmission range of the source and, thus, only the signals received in Phase (2) II, i.e., yd , is utilized for detection at the destination. Given the instantaneous knowledge of both Hs,r and Hr,d , the capacity of the AF MIMO relay channel [21] is given by   1 Ps Pr H H H −1 C = log2 det IMs + Hs,r F Hr,d Rw Hr,d FHs,r 2 Ms  Ps Pr H H H 1 H F Hr,d = log2 det IMs + 2 Ms s,r  +−1 * × σd2 IMd + Pr σr2 Hr,d FFH HH H FH r,d s,r r,d  Ps (a) 1 = log2 det IMs + HH 2 Ms σr2 s,r    −1  Pr σr2 H H Hs,r × IMr − IMr + 2 F Hr,d Hr,d F σd   Ps Ps 1 H H −1 , (7.57) H H − H H Q = log2 det IMr + s,r s,r s,r s,r 2 Ms σr2 Ms σr2 where (a) follows from matrix inversion lemma and Q = IMr + (Pr σr2 /σd2 ) ·FH HH r,d Hr,d F. Moreover, the term inside the determinant in (7.57) can be rewritten as Ps Ps −1 Hs,r HH Hs,r HH s,r − s,r Q Ms σr2 Ms σr2   Ps H = IMr + IMr + Hs,r Hs,r (IMr − Q−1 ) − (IMr − Q−1 ) Ms σr2     Pr σr2 H H Ps H Hs,r Hs,r F Hr,d Hr,d F Q−1 = IMr + IMr + Ms σr2 σd2

IMr +

and, thus, the channel capacity is given by   ⎞  ⎛ Pr σr2 H H Ps H + I + H H F H H F det I 2 M M 2 s,r r,d r r s,r r,d Ms σr 1 σd ⎠.   C = log2 ⎝ Pr σr2 H H 2 det IM + 2 F H Hr,d F r

σd

r,d

(7.58) By taking the singular-value decomposition (SVD) of the channel matrices Hs,r and Hr,d , we have H Hs,r = Us,r Λs,r Vs,r ,

(7.59)

H Ur,d Λr,d Vr,d ,

(7.60)

Hr,d =

308

7 Cooperation Relaying in OFDM and MIMO Systems

where Us,r , Ur,d , Vs,r , and Vr,d are unitary matrices, Λs,r is an Mr × Ms matrix with the first κs,r diagonal elements being the singular values λs,r (1) ≥ λs,r (2) ≥ · · · ≥ λs,r (κs,r ), and Λr,d is an Md × Mr matrix with the first κr,d diagonal terms being the singular values λr,d (1) ≥ λr,d (2) ≥ · · · ≥ λr,d (κr,d ). Here, κs,r and κr,d are the ranks of the matrices Hs,r and Hr,d , respectively. It has been shown in [21] that the optimal precoder that maximizes the channel capacity in (7.58) takes on the form F = Vr,d ΛF UH s,r ,

(7.61)

where ΛF  diag(f1 , f2 , · · · , fMr ) is an Mr × Mr diagonal matrix. Hence, the r problem is reduced to finding the optimal set of weighting factors {fi }M i=1 . By substituting (7.59), (7.60), and (7.61) into the channel capacity expression in (7.58), we have  ⎞   ⎛ Pr σr2 H H det IMr + IMr + MPssσ2 Λs,r ΛH 2 ΛF Λr,d Λr,d ΛF s,r 1 σ r d ⎠   C = log2 ⎝ Pr σr2 H H 2 det IM + 2 Λ Λ Λr,d ΛF =

=

Mr 1

2

log2 ⎝

i=1

Mr 1

2

⎛

r

1+

(1 + 1+

⎛ log2 ⎝1 +

i=1

Pr σr2 σd2

σd

F

r,d

Ps 2 2 2 Ms σr2 |λs,r (i)| )|λr,d (i)| |fi | Pr σr2 |λr,d (i)|2 |fi |2 σd2

⎞ ⎠

⎞

Pr Ps |λ (i)|2 |λr,d (i)|2 |fi |2 Ms σd2 s,r ⎠. P σ2 1 + σr 2 r |λr,d (i)|2 |fi |2 d

(7.62)

Note that λs,r (i) = 0 for i > κs,r and λr,d (i) = 0 for i > κr,d . From the capacity expression in (7.62), one can see that the MIMO relay channel is decomposed into min(Ms , Mr , Md ) parallel AF relay channels. By substituting (7.59) and (7.61) into (7.56), the power constraint can be expressed as  Mr   Ps 2 2 σr + |λs,r (i)| |fi |2 ≤ 1. M s i=1

(7.63)

r The optimal weighting factors {fi }M i=1 that maximize the channel capacity in (7.62) is given by the water-filling solution [21]

|fi |2 =

 + σd2 2 + 4ρ ρ μ − ρ ρ − 2 , 1,i 2,i 1,i 1,i 2ρ2,i (1 + ρ1,i )σr2

(7.64)

where ρ1,i  MPssσ2 |λs,r (i)|2 , ρ2,i  Pr |λr,d (i)|2 , (x)+ = max(x, 0) and μ is set r to satisfy the power constraint in (7.63). Example: Comparison of Different MIMO Relay Precoders

7.5 Cooperation with MIMO Relays

309

9

Ergodic Capacity (bits/sec/Hz)

8

7

6

5

4 Optimal precoder Equal−gain Pseudo−matched filter Suboptimal precoder

3

2 0 10

1

10

2

10 Ps /(M s σr2 ) (dB)

3

10

4

10

Fig. 7.14 Comparison of ergodic capacity for different relay precoding strategies in a dual-hop MIMO relay system with Ms = Mr = Md = 4 and with fixed relay transmission power Pr /(Mr σd2 ) = 10 dB.

In Fig. 7.14, the ergodic capacity of the optimal precoder is compared with those of three other relay precoding strategies: the equal-gain strategy, the pseudo-matched filter strategy proposed in [18], and a suboptimal precoder devised in [22]. The performance of these strategies are compared under the setting with Ms = Mr = Md = 4 and fixed relay power of Pr /(Mr σd2 ) = 10 dB. We assume that Hs,r and Hr,d ∼ CN (0, I) and σr2 = σd2 = 1. In the equal-gain scheme, the signals received  at the relay antennas are all amplified by a common weighting factor 1/ tr(σr2 IMr +(Ps /Ms )Hs,r HH s,r ). In the pseudo-matched filter method, the precoder is matched to the effective channel Hr,d Hs,r . By normalizing according to the power constraint, the precoding matrix for the pseudo-matched filter is given by ; < 1 < H  HH F==  r,d Hs,r . Ps H H 2 H tr Hs,r Hs,r (σr IMr + Ms Hs,r Hs,r )Hr,d Hr,d In the suboptimal precoding scheme, the precoding matrix is obtained by maximizing an upper bound of capacity in (7.57), and the upper bound can be achieved only at high SNR or when Hs,r is ill-conditioned [22]. The sub-

310

7 Cooperation Relaying in OFDM and MIMO Systems

optimal precoding solution also takes on the form in (7.61), but the weighting r factors {fi }M i=1 are chosen such that    φ2 |λs,r (i)|2 − φ1  1 2  , |fi | = μsub 1+ MPsσ2 |λs,r (i)|2  φ1 |λr,d (i)|2  s

r

Mr

Mr where φ1 = n=1 |λs,r (n)/λr,d (n)|2 , φ2 = Pσ2r + n=1 |λr,d (n)|−2 , and μsub is d set to satisfy the power constraint in (7.63). One can observe from Fig. 7.14 that the ergodic capacity achieved with the optimal precoder is larger than that of the other three precoding strategies, even as the source transmission power increases.

With Source-to-Destination Link When the destination is within the transmission range of the source, the channel capacity and, thus, the relay precoder design must take into consideration the signal received at the destination on the s-d link. By combining the signals received at the destination in both phases, we obtain ⎡ ⎤ (1)       w (1) Hs,d y I 0 0 ⎢ d ⎥ yd  d(2) = Ps √ xs + √ ⎣wr ⎦ 0 P H FH P H F I r r,d s,r r r,d yd (2) wd (7.65)  Ps Hxs + w, where

  Hs,d H= √ Pr Hr,d FHs,r

is the effective channel between the source and the destination and ⎡ ⎤  w(1)  I√ 0 0 ⎢ d ⎥ w= ⎣wr ⎦ 0 Pr Hr,d F I (2) wd is the effective noise with covariance matrix   2 0 σd IMd . Rw = 2 0 Pr σr2 Hr,d FFH HH r,d + σd IMd Given Hs,r , Hr,d , and Hs,d , the channel capacity can be computed as

(7.66)

7.5 Cooperation with MIMO Relays

   1 Ps H −1 C = log2 det IMs + H Rw H 2 Ms    Ps 1 H −1 . HH Rw = log2 det I2Md + 2 Ms

311

(7.67)

When the channel matrices Hs,r , Hr,d , and Hs,d are known at the relays, the precoding matrix F can be designed to maximize the capacity in (7.67) subject to the power constraint in (7.56). By expanding the matrix inside the bracket in (7.67), we have Ps I2Md + HHH R−1 w Ms √   Ps Pr H H : −1 Hs,d HH IMd + MPssσ2 Hs,d HH s,r F Hr,d Q s,d M s d = Ps √Pr Ps Pr H H H : −1 Hr,d FHs,r HH s,d IMd + Ms Hr,d FHs,r Hs,r F Hr,d Q Ms σd2   X11 X12 , (7.68)  X21 X22 : = Pr σr2 Hr,d FFH HH + σ 2 IM . By utilizing the identity where Q d r,d d det

  X11 X12 = det(X11 ) det(X22 − X21 X−1 11 X12 ), X21 X22

(7.69)

the channel capacity can be further written as [14]   Ps 1 H H H C = log2 det IMd + s,d s,d 2 Ms σd2   −1 1 Ps Pr Ps H + log2 det IMd + Hr,d FHs,r IMs + H H s,d 2 Ms Ms σd2 s,d  H H 2 H H 2 −1 . (7.70) ×HH s,r F Hr,d (Pr σr Hr,d FF Hr,d + σd IMd )

The first term in (7.70) is the capacity of the s-d link and, thus, does not depend on the relay precoder. Therefore, the optimal precoder F can be found by maximizing the second term.  − 12 Let G be defined as G  Hs,r IMs + MPssσ2 HH Hs,d with SVD given s,d d by H G = UG ΛG VG , (7.71) where UG is Mr × Mr unitary matrix, ΛG = diag(λg (1),λg (2),· · · ,λg (Mr )) and {λg (i)} are singular values of G in a non-increasing order. Similar to the previous case, the optimal precoder F takes on the form [14] F = Vr,d ΛF UH G,

(7.72)

312

7 Cooperation Relaying in OFDM and MIMO Systems

where the unitary matrix Vr,d is obtained by the SVD of Hr,d in (7.60) and the diagonal matrix ΛF = diag(f1 , f2 , · · · , fMr ) consists of coefficients that are to be optimized [13,14]. By substituting (7.60) and (7.71) into the second term in (7.70), we can write the second term as  1 Ps Pr H H log2 det IMd + Λr,d ΛF ΛG ΛH G ΛF Λr,d 2 Ms  + * 2 2 H H −1 × σd IMd + Pr σr Λr,d ΛF ΛF Λr,d   Md Ps Pr 2 2 1 Ms |λr,d (i)λg (i)| |fi | . (7.73) = log2 1 + 2 2 i=1 σd + Pr σr2 |λr,d (i)|2 |fi |2 The optimal weighting factors that maximize the term in (7.73) then results in a water-filling solution given by ' |fi | = 2

4ρ2,i ρg,i σr2 /σd2 · (ρg,i + σr2 ) +μ· − ρg,i − 2σr2 Ps /Ms · |λs,r (i)|2 + σr2 +−1 * × 2ρ2,i (ρg,i + σr2 )σr2 /σd2

+

ρ2g,i

(7.74)

Ps |λg (i)|2 , ρ2,i  Pr |λr,d (i)|2 , and μ is set to satisfy the power where ρg,i  M s constraint in (7.63). If only local CSI is available at the relay, the precoder can be optimized based only on the instantaneous CSI of Hs,r and Hr,d . In this case, the relay would not be able to compute the SVD of the matrix G and, thus, the solution in (7.72) is not attainable. To find a suboptimal precoder in this case, one can instead consider upper and lower bounds of the channel capacity in (7.67), whose optimization does not depend on knowledge of Hs,d . These bounds have been derived in [13, 14] and are summarized as follows. Firstly, by the matrix inequality   X11 X12 det ≤ det(X11 ) det(X22 ), (7.75) X21 X22

and by substituting (7.68) into the capacity expression in (7.67), the channel capacity can be upper bounded by 1 1 log2 det(X11 ) + log2 det(X22 ) 2 2    1 Ps 1 H Hs,d Hs,d + log2 det IMd = log2 det IMd + 2 Ms σd2 2  Ps Pr H H 2 H H 2 −1 . + Hr,d FHs,r HH F H (P σ H FF H + σ I ) r r,d M s,r r,d r r,d d d Ms (7.76)

C≤

7.5 Cooperation with MIMO Relays

313

Note that the first term depends only on the channel of the s-d link and the second term depends only on the channels of the s-r and the r-d links. On the other hand, the lower bound of the capacity can be obtained by utilizing the fact that   X11 X12 det = det(X22 ) det(X11 − X12 X−1 (7.77) 22 X21 ), X21 X22 where the term inside the second determinant is the Schur complement of the matrix in (7.68), and is given by X11 − X12 X−1 22 X21  −1 Ps Pr H H H : −1 Ps H I+ = I+ H H F H H FH Q s,d r,d s,r Hs,d . r,d Ms σd2 Ms s,r It has been shown in [14] that det(X11 − X12 X−1 22 X21 ) ≥ 1 in this case. Therefore, the capacity is lower bounded by 1 log2 det(X22 ) 2  1 Ps Pr H H = log2 det IMd + Hr,d FHs,r HH s,r F Hr,d 2 Ms

C≥

 2 −1 . ×(Pr σr2 Hr,d FFH HH + σ I ) r,d d Md

(7.78)

From (7.76) and (7.78), one can see that both upper and lower bounds of the channel capacity are determined by the determinant of the matrix X22 , which depends only on Hs,r and Hr,d . Hence, when only Hs,r and Hr,d are available at the relays, the suboptimal precoder can be designed to maximize the determinant of X22 . It has been shown in [14] that this precoding matrix is the same as that obtained in (7.61) and (7.64) [13, 14]. That is, when the channel of the s-d link is not available at the relays, the precoder that maximizes the upper and lower bounds are the same as the optimal precoder obtained without combining the s-d link. If the relay does not have any knowledge of the channel matrices, space-time codes can be applied at both the source and relays, e.g., in [25]. Moreover, the MIMO relaying strategy can also be extended to the case with multiple relays in [1, 26] and to the case with multiple sources and multiple destinations in [4]. Example: Comparison of Cooperative and Dual-Hop MIMO transmissions Consider a MIMO AF-based three-node cooperative system with each node employing 4 antennas (Ms = Mr = Md = 4), we compare the ergodic capacity of systems utilizing optimal and equal-gain precoders along with cooperative (with source-to-destination link) or dual-hop (without source-to-

314

7 Cooperation Relaying in OFDM and MIMO Systems

30 Optimal precoder Equal−gain With diversity

Ergodic Capacity (bits/sec/Hz)

25

20

15 Without diversity 10

5

0 0 10

1

10

2

10 Ps /(M s σr2 ) (dB)

3

10

4

10

Fig. 7.15 Comparison of ergodic capacity for cooperative and dual-hop transmissions in a MIMO cooperative relay system with Ms = Mr = Md = 4 and equal power allocation (Ps = Pr ).

destination link) transmissions in Fig. 7.15. Here, we deploy equal transmission power in the two phases (Ps = Pr ) and show the ergodic capacity as a function of Ps /Ms√ /σr2 . Also, it is assumed that Hs,r and Hr,d ∼ CN (0, I), Hs,d ∼ CN (0, 1/2 2 · I), and σr2 = σd2 = 1. As observed from Fig. 7.15, with the destination located relatively far from the source and the relay, cooperative transmission increases the capacity by about 4 bits/Hz/sec, compared to dual-hop transmission. Moreover, the equal-gain precoder in some sense wastes the multiplexing degrees of freedom at low SNR and provides merely a comparable performance with that of dual-hop transmission using optimal precoder.

References 1. Adinoyi, A., Yanikomeroglu, H.: Cooperative relaying in multi-antenna fixed relay networks. IEEE Transactions on Wireless Communications 6(2), 533–544 (2007) 2. Boleskei, H., Paulraj, A.: Space-frequency coded broadband OFDM systems. In: Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 1–6 (2000)

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3. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004) 4. Chalise, B.K., Vandendorpe, L.: MIMO relay design for multipoint-to-multipoint communications with imperfect channel state information. IEEE Transactions on Signal Processing 57(7), 2785–2796 (2009) 5. Gunduz, D., Goldsmith, A., Poor, H.: Diversity-multiplexing tradeoffs in MIMO relay channels. In: Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), pp. 1–6 (2008) 6. Hammerstr¨ om, I., Wittneben, A.: On the optimal power allocation for nonregenerative OFDM relay links. In: IEEE International Conference on Communications, vol. 10, pp. 4463 – 4468 (2006) 7. Han, Z., Himsoon, T., Siriwongpairat, W.P., Liu, K.J.R.: Resource allocation for multiuser cooperative OFDM networks: Who helps whom and how to cooperate. IEEE Transactions on Vehicular Technology 58(5), 2378–2391 (2009) 8. Laneman, J.N., Wornell, G.W.: Distributed space-time-coded protocols for exploiting cooperative diversity in wireless networks. IEEE Transactions on Information Theory 49(10), 2415–2425 (2003) 9. Li, Y., Wang, W., Kong, J., Peng, M.: Subcarrier pairing for amplify-and-forward and decode-and-forward OFDM relay links. IEEE Communications Letters 13(4), 209–211 (2009) 10. Li, Y., Zhang, W., Xia, X.-G.: Distributive high-rate full-diversity space-frequency codes for asynchronous cooperative communications. In: Proc. on IEEE International Symposium on Information Theory (ISIT), pp. 2612–2616 (2006) 11. Li, Y., Zhang, W., Xia, X.-G.: Distributive high-rate space-frequency codes achieving full cooperative and multipath diversities for asynchronous cooperative communications. IEEE Transactions on Vehicular Technology 58(1), 207–217 (2009) 12. Liang, Y., Schober, R.: Cooperative amplify-and-forward beamforming for OFDM systems with multiple relays. In: IEEE International Conference on Communications (ICC), pp. 1–6 (2009) 13. Munoz, O., Vidal, J., Agustin, A.: Non-regenerative MIMO relaying with channel state information. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 3, pp. 361–364 (2005) 14. Munoz-Medina, O., Vidal, J., Agustin, A.: Linear transceiver design in nonregenerative relays with channel state information. IEEE Transactions on Signal Processing 55(6), 2593–2604 (2007) 15. O’Hara, B., Petrick, A.: The IEEE 802.11 Handbook: A Designer’s Companion. IEEE Press, New York (1999) 16. Seddik, K.G., Liu, K.J.R.: Distributed space-frequency coding over amplify-andforward relay channels. In: Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), pp. 356–361 (2008) 17. Seddik, K.G., Liu, K.J.R.: Distributed space-frequency coding over broadband relay channels. IEEE Transactions on Wireless Communications 7(11), 4748–4759 (2008) 18. Sripathi, P.U., Lehnert, J.S.: A throughput scaling law for a class of wireless relay networks. In: Proceedings of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1333–1337 (2004) 19. Su, W., Safar, Z., Liu, K.J.R.: Full-rate full-diversity space-frequency codes with optimum coding advantage. IEEE Transactions on Information Theory 51(1), 229–249 (2005) 20. Sung, K.-Y., Hong, Y.-W. P., Chao, C.-C.: Resource allocation and partner selection for wireless cooperative multicarrier systems. submitted to Transactions on Wireless Communications (2009) 21. Tang, X., Hua, Y.: Optimal design of non-regenerative MIMO wireless relays. IEEE Transactions on Wireless Communications 6(4), 1398–1407 (2007) 22. Tang X. Hua, Y.: Optimal waveform design for MIMO relaying. In: Proceedings of IEEE 6th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 289–293 (2005)

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23. Wang, B., Zhang, J., Host-Madsen, A.: On the capacity of MIMO relay channels. IEEE Transactions on Information Theory 51(1), 29–43 (2005) 24. Wang, W., Wu, R.: Capacity maximization for OFDM two-hop relay system with separate power constraints. IEEE Transactions on Vehicular Technology 58(9), 4943– 4954 (2009) 25. Yang, S., Belfiore, J.-C.: Optimal space-time codes for the MIMO amplify-and-forward cooperative channel. In: Proceedings of the IEEE International Zuirch Seminar on Communications (IZS), pp. 122–125 (2006) 26. Yilmaz, A.: Cooperative multiple-access in fading relay channels. In: IEEE International Conference on Communications (ICC), vol. 10, pp. 4532–4537 (2006)

Chapter 8

Medium Access Control in Cooperative Networks

Most works in the literature on cooperative communications have focused mainly on the physical layer aspects such as coding, modulation, MIMO signal processing techniques etc, as described in the previous chapters. However, in practical systems, there may be multiple users that have the need to access the channel and therefore a proper design of medium access control (MAC) protocols is necessary to fully exploit the diversity advantages in cooperative networks. In this chapter, we discuss how MAC layer protocols can be modified to incorporate cooperation in the physical layer and what are the advantages of cooperation when viewed from a MAC layer perspective. These studies involve the consideration of stochastic packet arrivals, queueing dynamics, and user interactions. We first consider the cooperative slotted ALOHA protocol in Section 8.1 and its enhancements in collision resolution with cooperation in Section 8.2. Then, we discuss in Section 8.3 how simple modifications to IEEE 802.11 legacy systems can be made to support cooperation. In Section 8.4, cooperative transmission is exploited to enhance the efficiency of the distributed Automatic Retransmission reQuest (ARQ). Finally, throughput optimal scheduling policies based on the conventional maximum differential backlog algorithm is described in Section 8.5.

8.1 Cooperation with Slotted ALOHA To investigate the advantages of cooperation in the MAC layer, let us first focus our studies on the fundamental slotted ALOHA random access protocol [1, 3], where each user decides whether or not to transmit in each time slot based only on the outcome of a local coin toss. In such networks, no central controller exists to schedule the users’ transmissions and, thus, it is not immediately obvious that cooperation may be beneficial in these cases due to lack of coordination among users. However, it was shown in [9–11] that the

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_8, © Springer Science+Business Media, LLC 2010

317

318

8 Medium Access Control in Cooperative Networks

AP

ψn,D pn

ψk,D pk

ψα(k),D pα(k) ψk,α(k) ψα(k),k

λn

λk single user

λα(k) cooperating pair

Fig. 8.1 Slotted ALOHA system with cooperative and non-cooperative users (From Hong, c Lin and Wang; [2010] IEEE ).

advantages may in fact be substantial in terms of increasing the achievable stable throughput. We summarize these results in the following. Consider a wireless slotted ALOHA random access network with N users communicating to a common access point (AP) through independent fading channels as shown in Fig. 8.1. The network consists of multiple cooperative pairs as well as non-cooperative users. We consider the case of pairwise cooperation, where cooperation occurs only between pairs of cooperative users. The system time is slotted with duration equal to the transmission time of a packet. Let Atot k [m] be the total number of packets arriving at user k in the m-th time slot, which may include an exogenous packet or a packet received from its partner. The packets arriving at user k are stored in its local buffer, which we denote by bufferk . If bufferk is non-empty at the beginning of the time slot, user k will transmit a packet with probability pk . Here, we consider the collision channel with transmission errors, where we define the probability ψk,D to represent the probability that a transmission made by user k is correctly received by the AP given that no other user is transmitting. If more than one user is transmitting in the same time slot, the users will collide and no packet will go through. The probability ψk,D is used to model the effect of fading in a wireless system and is referred to as the correct reception probability. At the end of each time slot, the AP will send a (0, 1, e) feedback to all users, where 0 indicates that the slot was idle, 1 indicates that the transmission was successful, and e indicates that a transmission failed, due to either fading or collision. We assume that the feedback is always received correctly by the users. Let us first consider a basic cooperation scheme based on simple decodeand-forward (DF) relaying. Specifically, we assume that if the transmission of cooperative user k to the AP fails but is successfully overheard by its

8.1 Cooperation with Slotted ALOHA

319

partner, denoted as α(k), the partner will save the packet in its own buffer, treating it as one of its own, and retransmit the packet in later time slots with its own transmission probability pα(k) . On the other hand, if the AP successfully receives a packet of user k, the partner is not necessary to store the packet. We consider a half-duplex system where a user is able to receive a message from its partner only if it is not transmitting in the same time slot. To model the effect of fading on the inter-user channel, we define ψk,α(k) to be the correct reception probability from user k to user α(k), i.e., the probability that a packet sent by user k is correctly received by its partner α(k) given that no other user is transmitting. Moreover, to control the level of cooperation, we allow user α(k) to reject the inter-user packet arrival from user k with the rejection probability rα(k) . If partner α(k) is able to correctly decode the packet and does not choose to reject it, then it will store the packet in its own buffer and send an inter-user feedback to user k to acknowledge the reception of the packet. The packet is then dropped from user k’s local buffer. We would like to remark that the non-cooperative system is a special case of the cooperative system when we set rk = 1 for all k. To illustrate the cooperative advantages in the MAC layer, it is necessary to describe the queuing dynamics of this system. Let {Qk [m]}∞ m=0 be the queue length process of user k, where Qk [m] is the number of packets in bufferk at the beginning of the m-th time slot. The queue evolution can be described with the following equation: Qk [m + 1] = (Qk [m] − Sktot [m])+ + Atot k [m], ∞ tot ∞ where {Atot k [m]}m=0 and {Sk [m]}m=0 are the arrival and service processes of user k, and that (a)+ = max{a, 0}. More specifically, for a cooperative user k, the total packet arrival, i.e., Atot k [m], contains two parts: the exogenous packet arrival (denoted by Ak [m]) and the inter-user arrival from its partner (α(k)) α(k) (denoted by Ak [m]). Therefore, we have (α(k))

Atot k [m] = Ak [m] + Ak

[m].

(8.1)

Here, we assume that {Ak [m]}∞ m=0 is an independent and identically distributed (i.i.d.) Bernoulli process with mean λk , i.e., Ak [m] ∼ Bern(λk ), and is independent among users. The service process at user k is defined as (α(k))

Sktot [m] = Sk [m] + Sk where Sk [m] = Vk [m]



[m],

(1 − V [m]1{Q [m]>0} )Hk,D [m]

=k

corresponds to packets sent to the AP and

(8.2)

320

8 Medium Access Control in Cooperative Networks

⎡ (α(k))

Sk

[m] = Vk [m] ⎣



⎤ (1 − V [m]1{Q [m]>0} )⎦

=k

× (1 − Hk,D [m])(1 − Rα(k) [m])Hk,α(k) [m] corresponds to packets sent to its partner α(k). Here, 1{Qk [m]>0} is the indicator function that takes on the value 1 if Qk [m] > 0, and 0, otherwise. In the ∞ ∞ ∞ above, {Vk [m]}∞ m=0 , {Rα(k) [m]}m=0 , {Hk,D [m]}m=0 , and {Hk,α(k) [m]}m=0 are Bernoulli random processes that model the transmission attempt made by user k, the event that user α(k) rejects the packet from user k, and the errorfree transmission over the channels from user k to the AP and to the partner α(k), respectively. More specifically, we have Vk [m] ∼ Bern(pk ), Rα(k) [m] ∼ Bern(rα(k) ), Hk,D [m] ∼ Bern(ψk,D ), and Hk,α(k) [m] ∼ Bern(ψk,α(k) ). Moreover, since a packet departs from user k only if the queue is nonempty and that Sktot [m] = 1, the number of packets departing from user k in the m-th time slot (i.e., the departure process) can be expressed as Dktot [m] = Sktot [m]1{Qk [m]>0} . Similarly, we can also define (α(k))

Dk [m] = Sk [m]1{Qk [m]>0} and Dk

(α(k))

[m] = Sk

[m]1{Qk [m]>0} .

Since the number of packets arriving at user k over the inter-user channel is equal to the number of packets departing from user α(k) over the inter-user channel, it follows that (α(k))

Ak

(k)

[m] = Dα(k) [m].

Since Ak [m], Vk [m], Rα(k) [m], Hk,D [m], and Hk,α(k) [m] are all i.i.d. over time, the N -dimensional queue state Q[m] = (Q1 [m], . . . , QN [m]) will be independent of all past states when given Q[m]. Hence, {Q[m]}∞ m=0 forms an N -dimensional Markov process.

8.1.1 Definition of Stability Region To measure the performance of a queuing network, we are often interested in the set of arrival rates that the users can accommodate without becoming unstable (i.e., without having the queue length increase to infinity). The concept of queue stability is defined as follows. Definition 8.1 (System Stability [21,24]). Given the vector of system parameters ω = (p1 , · · · , pN , r1 , · · · , rN ) and the exogenous arrival rate vector λ = (λ1 , · · · , λN ), we say that the system is stable if

8.1 Cooperation with Slotted ALOHA

321

Fk (x) = lim Pr (Qk [m] < x) and m→∞

lim Fk (x) = 1, ∀k.

x→∞

(8.3)

Since the queues are interactive, the stability of each user depends on the arrival rates and the transmission probabilities of all users (as well as the rejection probabilities if the users are cooperative). Certainly, not all arrival rate vectors can be made stable with a given set of system parameters. Hence, we are interested in the set of arrival rate vectors that can be stabilized for at least one set of system parameters. This is referred to as the stability region. Suppose that C(ω) is the stability region obtained for a fixed set of system parameters ω. The closure of stability regions (or the so-called network capacity region) can then be defined as the union of stability regions over all possible values of the system parameters ω, i.e., E C≡ C(ω). (8.4) ω∈Ω

It is worthwhile to mention that, when the users are stable, the arrival rate will be equal to the departure rate, i.e., the throughput, of the users. Therefore, we may also refer to the arrival rates inside the stability region as the achievable stable throughput. To compute the stability region (or to further obtain the network capacity region) of a system, it is often convenient to construct an auxiliary system such that the stability of the auxiliary system implies the stability of the original system. In other words, the auxiliary system is stochastically dominant over the original system in the sense that * + Pr(Qk [m] > x) ≤ Pr Qdom [m] > x , for all k and m, k provided that the initial state is the same, i.e., Q[0] = Qdom [0]. Here, {Qdom [m]}∞ m=0 denotes the queue length process of user k in the auxiliary k system. Due to the stochastic dominance property, this system is referred to as a dominant system [21, 24]. The stability region of a dominant system is often easier to obtain and serves as an inner bound to the stability region of the original system. A typical way of constructing a dominant system is to assume that a set of users in the system is fully-loaded, which is to say that these users always have a packet to transmit regardless of its actual queue state (i.e., dummy packets will be transmitted by the user if its buffer is empty). A system with fully-loaded users is stochastically dominant over the true system since the dummy packets emitted by the fully-loaded users may cause additional congestion to the network and, thus, lead to longer queues. The stability region of the slotted ALOHA system is difficult to obtain in general. In fact, even in conventional networks with no cooperation, the region has only been completely characterized for the two and three user cases [21,24]. Bounds for the finite user system can be found in, e.g. [17,21]. In the following section, we derive the two-user stability region of a cooperative

322

8 Medium Access Control in Cooperative Networks

pair of users, which we label as user 1 and user 2. The two-user scenario is sufficient to illustrate the advantages of cooperation. Bounds for the finite user system can be found in [11, 12].

8.1.2 Stability Region of a Cooperative Pair To derive the two-user stability region, we apply Loynes’ theorem [16] to characterize the stability of each queue. With stationary arrival and service processes, Loynes’ theorem states that a queue (e.g. of user k) is stable if the tot total arrival rate, i.e., λtot k = E[Ak [m]], is less than the total service rate, tot tot tot 1 i.e., μk = E[Sk [m]], and that it is unstable if λtot k > μk . From (8.1) and (8.2), the total arrival and service rates for a cooperative user k are given as (α(k))

tot λtot k = E[Ak [m]] = E[Ak [m]] + E[Ak

(α(k))

[m]] = λk + λk

and (α(k))

tot μtot k = E[Sk [m]] = E[Sk [m]] + E[Sk

(α(k))

[m]] = μk + μk

,

where λk is the average number of exogenous packets arriving at user k in (α(k)) each time slot, λk is the average number of packets received by user (α(k)) k from its partner α(k), and μk , μk are portions of the average total service rate that correspond to packets sent to the AP and to its partner (α(k)) (k) α(k), respectively. Moreover, since Ak [m] = Dα(k) [m], it follows that (α(k))

λk

(k)

= μα(k) · Pr(Qα(k) [m] > 0).

Let us start out by considering a dominant system D{1,2} where all users (i.e., user 1 and user 2) are assumed to be fully-loaded. We shall refer to this as the fully-loaded system. In the fully-loaded system, both users access the channel with independent probabilities that remain the same over all time slots regardless of the queue states. Therefore, the stationarity of the arrival and service processes are trivially satisfied for each user and, thus, Loynes’ theorem can be applied as described above. More specifically, let us denote tot by λtot k,D{1,2} (ω) and μk,D{1,2} (ω) the total arrival and service rates of user k in the dominant system D{1,2} with system parameters ω. To simplify our notations, we shall suppress ω when the dependence is clear from the context. Since both users are assumed to be fully-loaded in D{1,2} , the average service rate of user k corresponding to packets sent to the AP is given by μk,D{1,2} = E[Sk,D{1,2} [m]] = pk (1 − pα(k) )ψk,D 1 The stability (or instability) of the queues cannot be guaranteed when the equality holds. However, in this work, only the interior of the stability region will be discussed.

8.1 Cooperation with Slotted ALOHA

323

and the inter-user service rate is given by (α(k))

(α(k))

μk,D{1,2} = E[Sk,D{1,2} [m]] = pk (1 − pα(k) )(1 − ψk,D )ψk,α(k) (1 − rα(k) ). Notice that, in D{1,2} , both users are fully-loaded and, thus, always have a packet to transmit. Therefore, from user 1’s (or user 2’s) perspective, the number of packets received from user 2 (or from user 1) in the m-th time (1) (2) slot is equal to S2 [m] (or S1 [m]), which may possibly be a dummy packet. (2) (1) (1) (1) (2) Hence, we have λ1,D{1,2} = E[S2 [m]] = μ2,D{1,2} and λ2,D{1,2} = E[S1 [m]] = (2)

μ1,D{1,2} . Consequently, given ω, the stability region of D{1,2} (or the so-called fullyloaded region) is CD{1,2} (ω)

0 (2) (1) tot = λ = (λ1 , λ2 ) ∈ R2+ : λ1 +λ1,D{1,2} < μtot and λ +λ < μ 2 1,D{1,2} 2,D{1,2} 2,D{1,2}

= λ ∈ R2+ : λ1 < p1 Φ1 (1−p2)−p2 Γ2 (1−p1) 0 and λ2 < p2 Φ2 (1−p1 )−p1 Γ1 (1−p2) , (8.5) where Γk  (1 − ψk,D )ψk,α(k) (1 − rα(k) )

(8.6)

is the probability that a packet successfully departs from user k to its partner α(k) given that user k is the only user transmitting, and Φk  ψk,D + (1 − ψk,D )ψk,α(k) (1 − rα(k) ) = ψk,D + Γk

(8.7)

is the probability that a packet successfully departs from user k (either to the AP or to its partner) given that user k is the only user transmitting. The region takes on a rectangular shape on the λ1 -λ2 plane, as shown in Fig. 8.2. As discussed in Section 8.1.1, fully-loaded regions in general can only serve as inner bounds to the true stability region. However, in the following, we show that the union of the fully-loaded regions coincides with the network capacity region for the two-user case. The proof can be found in [11]. Lemma 8.1. (a) Given ω, the cooperative two-user stability region is E C(ω) = CD{1} (ω) CD{2} (ω), where

324

8 Medium Access Control in Cooperative Networks

λ2

μtot 2,D

(1)

−λ2,D

{1,2}

{1,2}

xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxx μtot 1,D

(2)

−λ1,D

{1,2}

{1,2}

λ1

Fig. 8.2 Illustration of the stability region for the fully-loaded system under the transc IEEE.) mission parameter ω, i.e., CD{1,2} (ω). (From Hong, Lin, and Wang. 2010

CD{1} (ω) =

λ ∈ R2+ : λ2 < p2 Φ2 (1 − p1 ) − p1 Γ1 (1 − p2 ) and

λ1 0 and 0 otherwise. Similarly, the dynamics of a cooperative queue is ⎛ QS [m + 1] ≤ ⎝QS [m] − (d)

(d)

 (S,)∈OS

⎞+ R(S,) [m]⎠ + (d)



(d)

R(i,S) [m],

(i,S)∈IS

where OS ≡ {(S  , ) : (S  , ) ∈ S and S  = S} and IS ≡ {(i, S  ) : (i, S  ) ∈ T and S  = S} are the outgoing and incoming links at cooperative set S. Notice that there is no exogenous traffics going into a cooperative set. The network stability region is defined as in Definition 8.2 and can be characterized by the following theorem, which is a direct generalization of Theorem 8.2 [19] and that in [25].

8.5 Throughput Optimal Scheduling Protocols for Cooperative Networks

353

Theorem 8.3 (Cooperative Network Stability Region [29]). The network stability region Λ of a network G = (V, L,( S, T) ) with two-hop cooperative (d) forwarding is the set of all arrival rates ρ = ρk for which there exists k∈V

non-negative flow variables [f ]∈L∪S∪T ,d∈D that satisfy (d)

ρk =



(d)

fk,j +

(k,j)∈Ok

0=



(d)

fk,T −

(k,T )∈Tk





(d)

(d)



(d)

(d)

fS,k , ∀k ∈ V\d and ∀d ∈ D,

(S,k)∈Sk (d)

fk,d +

k∈Id



fi,S , ∀S ∈ S and ∀d ∈ D,

(i,S)∈IS

ρk =

k∈V

(d)

fi,k −

(i,k)∈Ik

fS,j −

(S,j)∈OS







(d)

fS,d , ∀d ∈ D,

(S,d)∈Sd

and 

(d)

f

≤ R , ∀ ∈ L ∪ S ∪ T where R = [R ]∈L∪S∪T ∈ Γ.

d∈D

Following the approaches given in [19,25], the policy below has been shown to stably support any set of arrival rates that lie within the cooperative stability region. Dynamic Rate Allocation/Routing Policy with Cooperation: 1. For all links (u, v), find commodity d∗u,v [m] such that d∗u,v [m] = arg max Qu(d) [m] − Qv(d) [m], d∈D

and define

d∗u,T [m]

= arg max Qu(d) [m] − |T | · QT [m],

d∗S,v [m]

= arg max |S| · QS [m] − Qv(d) [m],

(d)

d∈D

(d)

d∈D

4 d∗ [m] 5 d∗ [m] ∗ Wu,v [m] = max Quu,v − Qvu,v , 0 , 4 d∗ [m] 5 d∗ [m] ∗ Wu,T [m] = max Quu,T − |T | · QTT ,v , 0 , 4 5 d∗ [m] d∗ [m] ∗ WS,v [m] = max |S| · QSS,v − QvS,v , 0 .

2. Rate Allocation: Choose a rate matrix r such that    ∗ ∗ ∗ r = arg max ru,v Wu,v [m] + ru,T Wu,T [m] + rS,v WS,v [m]. r∈Γ

(u,v)

(u,T )

(S,v)

3. Routing: Data of commodity d is transmitted on link with rate

354

8 Medium Access Control in Cooperative Networks

(d)

R [m] =

r , if d = d∗ [m] and W∗ [m] > 0 0, otherwise.

for all ∈ L ∪ S ∪ T . (d)

(d)

(d)

(d)

Notice that the terms Qu [m] − |T | · QT [m] and |S| · QS [m] − Qv [m] reflect the queue coupling effect which does not occur in non-cooperative networks. The above policy was referred to as the Cooperative Maximum Differential Backlog (CMDB) policy and the proof of its optimality can be found in [29]. It is worthwhile to note that the backpressure algorithm and many of its variants do not require the knowledge of the statistics or the long-term observation of the arrival or channel state processes. However, perfect implementation of the algorithm requires global knowledge of the current state of the channel and queue lengths which may be difficult to attain in practice. Moreover, the complexity of solving the rate allocation step may increase exponentially with the number of nodes in the network. Distributed implementation of these strategies can be found in [19] and cases with imperfect channel state or queue length information can be found in [14, 20].

Appendix 8.1: Collision and Transmission Probabilities in IEEE 802.11 Systems In this appendix, we summarize the throughput analysis for saturated IEEE 802.11 systems as presented in [4]. More specifically, we consider a network of N nodes where all nodes have at least one packet awaiting for transmission in each time slot, i.e., the saturated condition. Let b[m] be the stochastic process that represents the backoff time counter for a given node. The time index of this process increments by 1 only when the backoff counter changes value. Since the backoff counter may pause when the channel is sensed to be busy, the time index of this process does not correspond to the discrete timescale of the 802.11 process which has fixed duration σ. Let CWmin and CWmax = 2M CWmin be the minimum and maximum contention window size, where M is the maximum backoff stage. Moreover, let s[m] be the stochastic process representing the backoff stage at time m, where s[m] ∈ {0, 1, . . . , M }. Suppose that p is the probability of collision given that a packet is transmitted by the given node. It is assumed that p is independent and fixed for each user and over different time slots. Under this assumption, the two-dimensional process {(s[m], b[m])}∞ m=0 can then be represented as a discrete time Markov Chain as shown in Fig. 8.15. As shown in the Markov Chain, a transmission attempt will be made by the node if it is in one of the states (i, 0), for i = 0, . . . , M . A transmission fails with probability p due to collision and a backoff counter is then

8.5 Throughput Optimal Scheduling Protocols for Cooperative Networks 1−p W0

1−p W0

0, 0

355

1

1

0, 1

p W1

0, 2

1

1

0, W0−1

1

i, Wi−1

1

M,WM−1

p W1

p Wi−1

i−1, 0 p Wi

p Wi

i, 0

1

p Wi+1

1

i, 1

i, 2

1

p Wi+1

M−1, 0 p WM

p WM

M, 0

1

M, 1

1

M, 2

1

p WM

p WM

c Fig. 8.15 Markov Chain model for random backoff mechanism (From Bianchi; [2000] IEEE ).

chosen uniformly within the set of integer values {0, . . . , Wi+1 − 1}, where Wi+1 = 2i+1 CWmin , given that it was in stage i before. The backoff counter is decremented by 1 after each idle time slot. Let πi,k = limm→∞ Pr(s[m] = i, b[m] = k), for i = 0, . . . , M and k = 0, . . . , Wi − 1 be the stationary distribution of the Markov Chain. First, note that πi−1,0 · p = πi,0 ⇒ πi,0 = pi π0,0 ,

for i = 1, . . . , M − 1

(8.22)

pM π0,0 . 1−p

(8.23)

and πM−1,0 · p + πM,0 · p = πM,0 ⇒ πM,0 =

Also, for each k = 1, . . . , Wi − 1, we have ⎧  Wi −k ⎪ (1 − p) M i=0 ⎨ W j=0 πj,0 , i Wi −k πi,k = pπ , i = 1, ...,M − 1 i−1,0 W ⎪ ⎩ Wi −k p(πi i = M. m−1,0 + πm,0 ), Wi By the fact that

M i=0

πi,0 =

π0,0 1−p ,

we can rewrite the above as

356

8 Medium Access Control in Cooperative Networks

πi,k =

Wi − k πi,0 , Wi

for i = 0, . . . , M and k = 0, . . . , Wi − 1.

(8.24)

With (8.22)-(8.24), all the steady state probabilities can be expressed in terms of π0,0 . Then, by the normalizing condition, we have M W i −1  

πi,k =

M 

W i −1 

M Wi − k  Wi + 1 = πi,0 W 2 i i=0 k=0 i=0 i=0 k=0    M−1  1 (2p)M π0,0 CWmin + . (2p)i + = 2 1−p 1−p i=0

1=

πi,0

By rearranging the terms, we get π0,0 =

2(1 − 2p)(1 − p) . (1 − 2p)(CWmin + 1) + p · CWmin (1 − (2p)M )

Using the steady state probabilities computed above, the transmission probability of the node in any given time slot can be computed as M 

2(1 − 2p) π0,0 = . 1 − p (1 − 2p)(CW + 1) + p · CWmin (1 − (2p)M ) min i=0 (8.25) Moreover, with τ , the probability of collision given that the node is transmitting can also be expressed as τ=

πi,0 =

p = 1 − (1 − τ )N −1

(8.26)

which is the probability that at least another node is transmitting. The probabilities τ and p can be solved uniquely with the nonlinear equations (8.25) and (8.26).

Appendix 8.2: Derivation of Probabilities In this appendix, we compute the probability that the optimum transmission scheme through a two-hop transmission uses rate Rx and Ry , given that the direct transmission is, e.g. 2 Mbps. First, note that the overlap area of two circles with radii dx and dy , given that the distance between the center of the circles is , as shown in Fig. 8.12, can be computed as     h h Sdx ,dy ( ) = d2x sin−1 + d2y sin−1 − h , dx dy

References

where h =

357

 2d2x d2y + 2(d2x + d2y ) 2 − (d4x + d4y ) − 4

G 2 . Given that dx is the

transmission radius of rate Rx and dy is the transmission radius of the rate Ry , the source will be able to utilize a two-hop transmission to the destination with rates Rx and Ry only if another nodes falls within the overlapping area of the two circles. By assuming that each node is uniformly distributed in the coverage area, i.e., the area associated with the minimum transmission rate 1 Mbps, the probability that a given node is located in this overlapping region is given by [15] q11,11 ( ) = q5.5,11 ( ) = q5.5,5.5 ( ) = q2,11 ( ) = q5.5,2 ( ) =

Sd11 ,d11 ( ) , πd21 2(Sd5.5 ,d11 ( ) − Sd11 ,d11 ( )) , πd21 Sd5.5 ,d5.5 ( ) + Sd11 ,d11 ( ) − 2Sd5.5 ,d11 ( ) , πd21 2(Sd2 ,d11 ( ) − Sd5.5 ,d11 ( )) , πd21 2(Sd2 ,d5.5 ( ) + Sd5.5 ,d11 ( )) 2(Sd2 ,d11 ( ) + Sd5.5 ,d5.5 ( )) − . πd21 πd21

The probability that the optimal transmission scheme is through a two-hop with rates Rx and Ry , given that the direct transmission rate is 2 Mbps, can be computed as 

2 [1 − q11,11 ( )]N −1 d , d22 − d25.5 d5.5  d2 2 [1 − q11,11 ( ) − q5.5,11 ( )]N −1 = 1 − p11,11 − d , d22 − d25.5 d5.5  d2 2 [1 − q11,11 ( ) − q5.5,11 ( ) − q5.5,5.5 ( )]N −1 = 1 − p11,11 − p5.5,11 − d , d22 − d25.5 d5.5

p11,11 = 1 − p5.5,11 p11,11

d2

for (x, y) = (11, 11), (5.5, 11), and (5.5, 5.5), respectively.

References 1. Abramson, N.: The Aloha system - Another alternative for computer communications. In: Proceedings of Fall Joint Computer Conference, AFIPS Conference, vol. 37, pp. 281-285 (1970) 2. Alonso-Z´ arate, J., Kartsakli, E., Verikoukis, C., Alonso, L.: Persistent RCSMA: A MAC protocol for a distributed cooperative ARQ scheme in wireless networks. EURASIP Journal on Advances in Signal Processing (2008) 3. Bertsekas, D., Gallager, R.: Data Networks, 2nd edn. Prentice Hall (1992)

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4. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications 18(3), 535–547 (2000) 5. Bletsas, A., Khisti, A., Reed, D.P., Lippman, A.: A simple cooperative diversity method based on network path selection. IEEE Journal on Selected Areas in Communications 24(3), 659–672 (2006) 6. Cali, F., Conti, M., Gregori, E.: IEEE 802.11 wireless LAN: Capacity analysis and protocol enhancement. In: Proceedings of IEEE INFOCOM, vol. 1, pp. 142–149 (1998) 7. Chou, C.-T., Yang, J., Wang, D.: Cooperative MAC protocol with automatic relay selection in distributed wireless networks. In: Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW), pp. 526–531 (2007) 8. Heusse, M., Rousseau, F., Berger-Sabbatel, G., Duda, A.: Performance anomaly of 802.11b. In: Proceedings of IEEE INFOCOM, vol. 2, pp. 836– 843 (2003) 9. Hong, Y.-W., Lin, C.-K., Wang, S.-H.: On the stability of two-user slotted ALOHA with channel-aware and cooperative users. In: 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks and Workshops (WiOpt), pp. 1–10 (2007) 10. Hong, Y.-W., Lin, C.-K., Wang, S.-H.: On the stability region of two-user slotted ALOHA with cooperative relays. In: Proceedings on the IEEE International Symposium on Information Theory (ISIT), pp. 356–360 (2007) 11. Hong, Y.-W. P., Lin, C.-K., Wang, S.-H.: Exploiting cooperative advantages in slotted ALOHA random access networks. to appear in IEEE Transactions on Information Theory (2010) 12. Lin, C.-K., Hong, Y.-W. P.: On the finite-user stability region of slotted ALOHA with cooperative users. In: Proc. on IEEE International Conference on Communications (ICC), pp. 1082–1086 (2008) 13. Lin, R., Petropulu, A.P.: A new wireless network medium access protocol based on cooperation. IEEE Transactions on Signal Processing 53(12), 4675–4684 (2005) 14. Lin, X., Shroff, N.B.: The impact of imperfect scheduling on cross-layer congestion control in wireless networks. IEEE/ACM Transactions on Networking 14(2), 302–315 (2006) 15. Liu, P., Tao, Z., Narayanan, S., Korakis, T., Panwar, S.: CoopMAC: A cooperative MAC for wireless LANs. IEEE Journal on Selected Areas in Communications 25(2), 340–354 (2007) 16. Loynes, R.: The stability of a queue with non-independent inter-arrival and service times. Proceedings of the Cambridge Philosophical Society 58, 497–520 (1962) 17. Luo, W., Ephremides, A.: Stability of N interacting queues in random-access systems. IEEE Transactions on Information Theory 45(5), 1579–1587 (1999) 18. Neely, M.J.: Dynamic power allocation and routing for satellite and wireless networks with time varying channels. Ph.D. thesis, Massachusetts Institute of Technology (2003) 19. Neely, M.J., Modiano, E., Rohrs, C.E.: Dynamic power allocation and routing for time-varying wireless networks. IEEE Journal on Selected Areas in Communications 23(1), 89–103 (2005) 20. Neely, M.J., Urgaonkar, R.: Optimal backpressure routing for wireless networks with multi-receiver diversity. Ad Hoc Networks 7(5), 862–881 (2009) 21. Rao, R., Ephremides, A.: On the stability of interacting queues in a multi-access system. IEEE Transactions on Information Theory 34(5), 918–930 (1988) 22. Sadeghi, B., Kanodia, V., Sabharwal, A., Knightly, E.: Opportunistic media access for multirate ad hoc networks. In: Proceedings of ACM MobiCom, pp. 24–35 (2002) 23. Sayed, S., Yang, Y.: BTAC: A busy tone based cooperative MAC protocol for wireless local area networks. In: International Conference on Communications and Networking in China (ChinaCom), pp. 403–409 (2008) 24. Szpankowski, W.: Stability conditions for some multiqueue distributed systems: Buffered random access systems. Advances in Applied Probability 26, 498–515 (1994)

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25. Tassiulas, L., Ephremides, A.: Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control 37(12), 1936–1948 (1992) 26. Tsatsanis, M.K., Zhang, R., Banerjee, S.: Network-assisted diversity for random access wireless networks. IEEE Transactions on Communications 48(3), 702–711 (2000) 27. Verde, F., Korakis, T., Erkip, E., Scaglione, A.: On avoiding collisions and promoting cooperation: Catching two birds with one stone. In: Proceedings of IEEE Workshop on Signal Processing for Advanced Wireless Communications (SPAWC), pp. 431–435 (2008) 28. Wu, H., Peng, Y., Long, K., Cheng, S., Ma, J.: Performance of reliable transport protocol over IEEE 802.11 wireless LAN: Analysis and enhancement. In: Proceedings of INFOCOM, vol. 2, pp. 559–608 (2002) 29. Yeh, E., Berry, R.: Throughput optimal control of cooperative relay networks. IEEE Transactions on Information Theory 53(10), 3827–3833 (2007)

Chapter 9

Networking and Cross-Layer Issues in Cooperative Networks

In the previous chapter, we have examined medium access control (MAC) layer issues in cooperative networks and have introduced several cooperative MAC protocols to help exploit cooperative advantages from the MAC layer. In this chapter, we take a further look at other higher-layer issues in cooperative networks, including quality-of-service (QoS), routing, and security. First, we examine the advantages of cooperation in terms of enhancing the QoS for networks that support, e.g., multimedia applications. The concept of effective capacity is utilized to characterize the QoS and conditions are provided on when a certain QoS requirement can be met. Secondly, we introduce the role of cooperative transmissions in network routing problems. Different from conventional routing problems, the search for the optimal route that takes into consideration the possibility of cooperative transmissions is intractable. Several cooperative routing protocols will be introduced and their advantages in terms of energy efficiency will be highlighted. Finally, we touch slightly upon security issues in cooperative networks that involve misbehaving relays or partners. While many works have been devoted to the study of this problem in higher layers, especially in the context of ad hoc networks, we focus more on the cross-layer approach where physical layer signal processing is utilized to identify malicious relays and to avoid its damages.

9.1 QoS in Cooperative Networks User cooperation has been shown, e.g., in Chapters 5 and 8, to increase Shannon capacity as well as network throughput. However, the study on capacity and throughput cannot accurately characterize the system’s ability to guarantee quality-of-service (QoS) requirements, such as transmission delay bounds or buffer overflow probabilities. In this section, we utilize the concept of effective capacity [22] to characterize the QoS of users in wireless systems. The notion of effective capacity stems from the concept of effective bandwidth [2,3],

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4_9, © Springer Science+Business Media, LLC 2010

361

362

9 Networking and Cross-Layer Issues in Cooperative Networks

A(t)

Q(t)

data source Qmax

S(t)

wireless channel

Fig. 9.1 Queueing model of a single queue.

originally studied in the context of wireline networks. In wireline networks, the transmission channel is assumed to be steady and reliable; therefore, the service rate can be modeled as constant while the packet arrival process is assumed to be stochastic. In this case, it is possible that the source buffer will exceed its limits, resulting in buffer overflow and packet drops. In systems with infinite buffer size and a constant service rate, the queue length can be viewed as a scale of the queuing delay and, thus, the buffer over flow probability also characterizes the probability that the delay exceeds a certain bound. Therefore, the buffer overflow probability serves as a good measure of QoS for different source arrival processes and different values of the constant service rate. Unfortunately, this probability is difficult to describe in general for finite buffer sizes. However, by utilizing large deviations theory, one can instead characterize the asymptotic decay rate of the buffer overflow probability as the buffer size Qmax increases. The effective bandwidth is defined as the constant service rate required to achieve the predetermined decay rate of the buffer overflow probability. Analogously, in wireless networks, where instead the service process is stochastic, we can characterize the asymptotic decay rate of the buffer overflow probability for a given constant arrival rate and define effective capacity as the maximum supportable arrival rate under a bound on the asymptotic decay rate of the buffer overflow probability. More specifically, let us consider a simple queueing model as shown in Fig. 9.1. Let A(t) be the amount of source data that have arrived at the buffer during the time interval [0, t) and let S(t) be the amount of data that have been served by the channel during the time interval [0, t). The queue length at time instant t is given by Q(t) = (A(t) − S(t))+ ,

(9.1)

where (x)+ = max(0, x). Assume that the asymptotic log-normal generating function of A(t), i.e., the function ( ) 1 log E eθA(t) , t→∞ t

Λ(θ)  lim

(9.2)

exists and is differentiable for all θ ≥ 0. Then, the effective bandwidth can be defined as ( ) Λ(θ) 1 1 B(θ)  = lim log E eθA(t) , (9.3) θ θ t→∞ t

9.1 QoS in Cooperative Networks

363

which is a function of the parameter θ. For a constant service rate μ, it follows by large deviations theory [2, 3] that the buffer overflow probability behaves as sup Pr(Q(t) ≥ Qmax ) ∼ e−θ(μ)Qmax ,

(9.4)

t

where f (x) ∼ g(x) means that limx→∞ f (x)/g(x) = 1 and Qmax is the buffer size. Here, θ(μ) is the asymptotic decay rate of the buffer overflow probability under the constant service rate μ and is shown to be the solution of B(θ) = μ [2, 3]. Hence, the effective bandwidth B(θ) can be viewed as the minimum constant service rate required to achieve the asymptotic decay rate of θ. The value of θ is often used to characterize the achievable QoS and, thus, is referred to as the QoS exponent. For example, in multimedia applications, where delay is the main concern, we can view equivalently the buffer overflow probability in (9.4) as the probability that the delay D(t) exceeds some delay requirement Dmax , i.e., sup Pr(D(t) ≥ Dmax ) ∼ e−θ(μ)μDmax ,

(9.5)

t

since D(t) = Q(t)/μ and Dmax = Qmax /μ. Then, a tolerable bound on the probability of delay-bound violation can be translated (by (9.4)) into a bound on the QoS exponent θ. The value of θ is set according to the QoS guarantees for different applications. For example, for voice and video applications, the decay rate θ must be large while, for best-effort data (e.g., file transportation or email delivery), the value of θ can be relatively small. On the other hand, by assuming that the arrival rate is constant and that the service process is variable and stochastic, the effective capacity can be defined analogously as the constant arrival rate λ that can be accommodated while satisfying a constraint on the asymptotic decay rate of the buffer overflow probability or the delay-bound violation probability. Similarly, suppose that the function  ( ) 1 Γ(−θ) = lim log E e−θS(t) t→∞ t exists and is differentiable for all θ ≥ 0. The effective capacity for a given value of θ is defined as )  ( 1 1 Γ(−θ) (9.6) = − lim log E e−θS(t) . C(θ)  − θ θ t→∞ t By large deviations theory, the buffer overflow probability can also be described as sup Pr(Q(t) ≥ Qmax ) ∼ e−θ(λ)Qmax , (9.7) t

364

9 Networking and Cross-Layer Issues in Cooperative Networks

γs,d Source

γs,r

γr,d

Destination

Relay Fig. 9.2 Two-user relay network model.

where the asymptotic decay rate θ(λ) now satisfies C(θ) = λ. Since the arrival rate is constant with rate λ, the data served at time t has actually arrived Q(t)/λ time earlier and, thus, the delay of this data is D(t) = Q(t)/λ. Hence, the delay-bound violation probability is also given by sup Pr(D(t) ≥ Dmax ) ∼ e−θ(λ)λDmax .

(9.8)

t

Note that, as θ goes to zero, the delay bound will approach infinity for a fixed constraint on the delay-bound violation probability. In this case, no delay constraint will exist and the effective capacity will be equivalent to the average channel capacity that is maximized by performing rate and power allocation over time. On the other hand, as θ approaches infinity, the system cannot tolerate any delay and, thereby, a constant throughput should be provided by the channel. The effective capacity in this case is equivalent to the zero-outage probability and is achieved by choosing power that scales with the inverse of the channel gain at each time instant [20]. Furthermore, it is worthwhile to note that the effective bandwidth B(θ) increases with θ whereas the effective capacity C(θ) decreases with θ. This coincides the intuition that a larger service rate or a smaller arrival rate is required to achieve more stringent QoS guarantee. In wireless networks, the service rate of each user varies dramatically over time due to the time-varying characteristics of the fading channel. With cooperation, the increased spatial degrees of freedom can be utilized to provide either higher reliability or larger throughput. Hence, for a given QoS exponent θ, it is interesting to see how much the users can benefit from cooperation in terms of maximizing the constant arrival rate supportable by the system. To study the cooperative advantages, we first consider a simple three-node relay network, as shown in Fig. 9.2, and derive the optimal power allocation that maximizes the effective capacity of this system. Then, we examine the effective capacity region of a two-user pair-wise cooperative system under a sum power constraint among the two users. The advantages of cooperation and effect of resource competition can be observed from the region that we obtain.

9.1 QoS in Cooperative Networks

365

9.1.1 QoS of a Simple Relay Network Consider a simple relay network that consists of a source, a relay, and a destination as shown in Fig. 9.2. Suppose that time is divided into equallength time frames of duration Tf and assume that the channel coefficients remain constant throughout the transmission of each frame, but is independently and identically distributed (i.i.d.) over different time frames. Specifically, let hs,d [m], hs,r [m], and hr,d [m] be the channel coefficients on the s-d, s-r, and r-d links respectively, in the m-th time frame and let N0 be the noise variance. Then, we can define the SNR of the s-d, s-r, and r-d links as γs,d [m]  |hs,d [m]|2 /N0 W , γs,r [m]  |hs,r [m]|2 /N0 W and γr,d [m]  |hr,d [m]|2 /N0 W , respectively, and let γ[m]  (γs,d [m], γs,r [m], γr,d [m]) be the vector of link SNRs. In general, the capacity achievable in each time frame, e.g., C(γ[m]), can be expressed as a function of the link SNRs γ[m]. If the units of C(γ[m]) is in bits/sec, then the number of bits that can be transmitted in the m-th time slot can be expressed as Rs [m] = Tf C(γ[m]). The total number of bits served up to time t = M Tf is equal to S(t) =

M  m=1

Rs [m] =

M 

C(γ[m])Tf .

m=1

Hence, by (9.6) and by assuming that the channel is i.i.d. over time, the effective capacity can be computed as )  ( M 1 1 lim log E e−θ m=1 C(γ[m])Tf θ M→∞ M Tf  ( ) 1 = − log E e−θTf ·C(γ) , θ

C(θ) = −

(9.9)

where the frame index m is omitted in the last equality since the channel is assumed to be i.i.d.. The effective capacity that can be attained in cooperative networks depends on the channel capacity C(γ), which in order depends on the link SNR vector γ and the employed relay strategy. Let us adopt a two-phase cooperative scheme where each frame is divided into two transmission periods. In the first period, the source broadcasts its message to both the relay and the destination while, in the second period, the relay forwards the source’s message to the destination. We assume that full CSI is available at the transmitters so that the rate and power can be adapted according to their instantaneous channel conditions. Specifically, we can define Ps (γ) and Pr (γ) to be the powers transmitted by the source and the relay, respectively, under the given channel condition γ. By employing

366

9 Networking and Cross-Layer Issues in Cooperative Networks

the basic AF relaying scheme (see Chapter 3 and [10]), the capacity can be expressed as   W γs,r Ps (γ)γr,d Pr (γ) CAF (γ) = log2 1 + γs,d Ps (γ) + , (9.10) 2 1 + γs,r Ps (γ) + γr,d Pr (γ) where the factor 1/2 is placed before the log-function since the same codeword is transmitted twice in the same time frame (c.f. Chapter 3). On the other hand, by employing the basic DF relaying scheme (c.f. Chapter 3 and [10]), the capacity can be written as

W CDF (γ) = min log2 (1 + γs,r Ps (γ)) , 2 0 log2 (1 + γs,d Ps (γ) + γr,d Pr (γ)) . (9.11) The capacity given above is in bits/sec. Hence, Rs (γ) = Tf C(γ) is the number of bits that can be transmitted in each frame, as mentioned above, where C(γ) is the capacity given by either (9.10) or (9.11). By the relation in (9.9), the effective capacity can be maximized by performing the optimal power allocation among the source and the relay subject to the average power constraint   1 1 E Ps (γ) + Pr (γ) ≤ P , 2 2 where the factor 1/2 is due to the fact that Ps (γ) and Pr (γ) are transmitted only over half the time frame. Let P(γ)  (Ps (γ), Pr (γ)) be the power allocation under the link SNR γ. In the AF scheme, the optimal power allocation policy can be found by [19] max P(γ)

 ( ) 1 − log E e−θTf ·CAF (γ) θ

(9.12)

subject to E [Ps (γ) + Pr (γ)] ≤ 2P , Ps (γ) ≥ 0, Pr (γ) ≥ 0, ∀γ. The solution is difficult to obtain since the objective in (9.12) is non-convex. However, at high SNR, we can obtain the following approximation: 1 + γs,r Ps (γ) + γr,d Pr (γ) ≈ γs,r Ps (γ) + γr,d Pr (γ). Therefore, the capacity under the AF scheme can be expressed as   W γs,r Ps (γ)γr,d Pr (γ) CAF (γ) ≈ log2 1 + γs,d Ps (γ) + 2 γs,r Ps (γ) + γr,d Pr (γ)

(9.13)

9.1 QoS in Cooperative Networks

367

which is now convex with respect to P(γ). By substituting the approximate capacity expression into (9.12), we can obtain the Lagrange function  − β2  γs,r Ps (γ)γr,d Pr (γ) L(P(γ)) = E 1 + γs,d Ps (γ) + γs,r Ps (γ) + γr,d Pr (γ) + νE [Ps (γ) + Pr (γ)] , where ν is the Lagrange multiplier and β

θTf W log 2

is the normalized QoS exponent. By KKT conditions, it follows that the derivatives of the Lagrange function must satisfy ∂L =0 ∂Ps (γ)

∂L =0 ∂Pr (γ)

and

if there exists solution P(γ) such that Ps (γ) > 0 and Pr (γ) > 0. Under these conditions, the solution can be found as Ps (γ) = uPr (γ) and

⎛

Pr (γ) =

1 ⎝ γ0 v γr,d



γr,d + c γs,d + c

−2 2  β+2

(9.14a) ⎞+ − 1⎠ .

(9.14b)

where the parameters are defined as c  γs,d γr,d + γs,r γr,d − γs,d γs,r , γr,d (γs,d + c) , γs,r (γr,d − γs,d ) cγr,d (γs,d + c)2 , v (γr,d − γs,d )γs,r (γr,d + c)

u

and γ0  μ/β. Note that the solution in (9.14) is feasible only when Pr (γ) > 0 and u > 0. Otherwise, the AF scheme will reduce to direct transmission and the optimal power allocation can be computed as [20] 

−2 β+2

Ps (γ) = γ0

−β β+2

γs,d −

−1 γs,d

+ and Pr (γ) = 0.

(9.15)

Here, γ0 is chosen to satisfy the power constraint E[Ps (γ) + Pr (γ)] ≤ 2P . Similarly, in the DF scheme, the optimal power allocation strategy can be found by

368

9 Networking and Cross-Layer Issues in Cooperative Networks

max P(γ)

)  ( 1 − log E e−θTf ·CDF (γ) θ

(9.16)

subject to E [Ps (γ) + Pr (γ)] ≤ 2P , Ps (γ) ≥ 0, Pr (γ) ≥ 0, ∀γ. By the monotonicity of the logarithmic function and by substituting CDF (γ) with (9.11), the optimization problem can be formulated equivalently as min

P(γ)

E [max{F1 (γ), F2 (γ)}]

(9.17)

subject to E [Ps (γ) + Pr (γ)] ≤ 2P , Ps (γ) ≥ 0, Pr (γ) ≥ 0, ∀γ, where F1 (γ) = (1 + γs,r Ps (γ))

−β 2

and F2 (γ) = (1 + γs,d Ps (γ) + γr,d Pr (γ))

−β 2

.

Note that the objective is strictly convex in the DF scenario and, thus, yields a unique solution. The solution can be obtained by considering two scenarios. First, if γs,r < γs,d , it follows that F1 (γ) > F2 (γ) regardless of the value of P2 (γ). Hence, we should set Pr (γ) = 0 and solve for Ps (γ) =

arg min

E[F1 (γ)],

(9.18)

Ps (γ):E[Ps (γ)]≤P

which yields the solution that +  −2 −β β+2 −1 − γs,r . Ps (γ) = γ0β+2 γs,r

(9.19)

On the other hand, when γs,r ≥ γs,d , the power should be found such that F1 (γ) = F2 (γ). This leads to the condition that Pr (γ) =

γs,r − γs,d Ps (γ). γr,d

(9.20)

By substituting (9.20) into the objective function, we can obtain the Lagrange function L(P(γ)) = E [F1 (γ)] + μE[Ps (γ) + Pr (γ)]    ( ) γs,r − γs,d −β = E (1 + γs,r Ps (γ)) 2 + μE 1 + Ps (γ) . γr,d By setting ∂L/∂Ps (γ) = 0, we arrive at the solution that

9.1 QoS in Cooperative Networks

369

(a) AF scheme

(b) DF scheme Fig. 9.3 Normalized effective capacity for AF and DF schemes. (From Tang and Zhang. c 2007 IEEE.)

 Ps (γ) =

γs,r − γs,d 1+ γr,d

and Pr (γ) =



−2  β+2

γ0

−β β+2

−1 γs,r − γs,r

γs,r − γs,d Ps (γ). γr,d

+ (9.21)

(9.22)

Again, γ0 is chosen such that the average power constraint is satisfied.

370

9 Networking and Cross-Layer Issues in Cooperative Networks

Fig. 9.4 Ratio of the effective capacity of DF scheme over that of AF scheme. (From Tang c and Zhang. 2007 IEEE.)

In Fig. 9.3, the normalized effective capacity of both AF and DF schemes are shown and compared with that of direct transmission (i.e. Direct TX). Here, the normalized effective capacity is defined as the effective capacity divided by the system bandwidth W and the frame duration Tf . The factor Tf W/ log 2 is set to 1 so that β = θ and the SNR on each link is assumed to be exponentially distributed with the mean proportional to the fourth power of link distance. The distance between the source and destination is normalized to 1 while the distances between source and relay and between relay and destination are given by d and 1 − d, respectively. The normalized effective capacity of the two-user relay network is plotted versus the distance factor d and the QoS exponent θ. When the QoS requirement is loose, i.e., when θ is small, the difference between the normalized effective capacities of the cooperative and non-cooperative systems are not apparent. However, large gains are observed with user cooperation when the QoS requirement becomes more stringent. This behavior is observed for both AF or DF schemes. Indeed, with user cooperation, the resources among the source and the relay can be utilized more flexibly to provide a more constant service rate. In Fig. 9.4, the ratio of the effective capacities between the DF and the AF schemes is shown with respect to the QoS exponent θ and the distance d. One can observe that, the DF scheme yields higher effective capacity when the relay is close to the source but the effective capacity degrades as the source and the relay become more distant. This is due to the fact that, in the DF scheme, the relay is required to decode the source’s messages and,

9.1 QoS in Cooperative Networks

371

User 1 γ1,d γ2,1

γ1,2 γ2,d

Destination

User 2 Fig. 9.5 Illustration of a two-user cooperative network.

therefore, the distance must be close enough to ensure a reliable source-torelay channel. If the source message is decodable, the relay is able to forward a clear message to the destination, which is in contrast to the AF scheme.

9.1.2 QoS of a Cooperative Pair In the previous subsection, we considered a simple relay network that consists of a designated source and relay. However, in a general cooperative network, the source and the relay may be two cooperative users that take turns in assuming one of the two roles. In this case, the transmission resources (e.g. power, bandwidth, etc.) must be shared among the two users and the achievable QoS will become a tradeoff among the two users. In this subsection, we consider a two-user cooperative pair, where both users may take turns in acting as the source, and examine the effective capacity region under all power allocation policies. Let us consider a two-user cooperative network as shown in Fig. 9.5. Assume that the two users are each assigned an orthogonal channel with bandwidth W and duration Tf . Each channel is further divided into two phases. In Phase I, both users act as sources and broadcast their own messages to each other and the destination. The users then immediately retransmit their partners’ messages in Phase II. Let γi,d  |hi,d |2 /N0 W be the SNR on the link between user i and the destination and let γi,j  |hi,j |2 /N0 W be the inter-user SNR on the link from user i to user j. Thus, the instantaneous CSI can be represented by the vector γ = (γ1,d , γ2,d , γ1,2 , γ2,1 ). Suppose that PiI (γ) and PiII (γ), for i ∈ {1, 2}, are the powers transmitted by user i in Phases I and II when the instantaneous CSI is γ. We shall consider an average sum power constraint by the two users given by   1 I 1 II 1 I 1 II E P1 (γ) + P1 (γ) + P2 (γ) + P2 (γ) ≤ P . (9.23) 2 2 2 2

9 Networking and Cross-Layer Issues in Cooperative Networks

Normalized Effective Capacity of user 2 (bits/sec/Hz)

372

3 AF DF Direct Tx

2.5 2 1.5 1 0.5 0 0

0.5

1

1.5

2

2.5

Normalized Effective Capacity of user 1 (bits/sec/Hz)

Fig. 9.6 Effective capacity region under θ = 0.01 for a cooperative pair.

By assuming that P 1 and P 2 are allocated to the transmission of user 1’s and user 2’s data, respectively, the effective capacity of each user can be maximized individually by solving the following two optimization problems:  ( ) 1 i) arg max − log E e−θTf C1 (γ) (9.24) θ P1 (γ)  subject to E P1I (γ) + P2II (γ) ≤ 2P 1 , P1I (γ) ≥ 0, P2II (γ) ≥ 0.  ( ) 1 (9.25) ii) arg max − log E e−θTf C2 (γ) θ P2 (γ)  subject to E P2I (γ) + P1II (γ) ≤ 2P 2 , P2I (γ) ≥ 0, P1II (γ) ≥ 0. Recall that Ci (γ) is the instantaneous channel capacity of user i under the CSI γ and PiII is the power utilized to relay the other user’s data. By adopting either the AF or the DF schemes, the channel capacity can be represented by either (9.10) or (9.11) and the optimal power allocation follows from the results of the previous section. Clearly, the effective capacity of one user increases when it is allocated more power while that of the other user decreases. By considering all possible values of (P 1 , P 2 ) such that P 1 + P 2 = P , we can obtain the boundary of the two-user normalized effective capacity region, as shown in Fig. 9.6. Each point on the boundary is achieved by performing optimal power allocation over a pair of sub-constraints (P 1 , P 2 ). In this experiment, we set the total average power as P = P 1 + P 2 = 10 and assume that the link SNRs γ1,d , γ2,d , γ1,2 ,

9.2 Routing in Cooperative Networks

373

and γ2,1 are exponentially distributed with means 1, 4, 10 and 10. In this case, user 1 has a worse uplink channel than user 2. Hence, with user cooperation, the normalized effective capacity of user 1 can be increased with the help of user 2. However, the effective capacity of user 2 is decreased since the relay path provided by user 1 is less reliable than its direct link to the destination and, thus, the time allocated to Phase II is not utilized efficiently. To improve the cooperative advantages of the two users, one can further adjust the time allocated to Phases I and II of the cooperative transmission. Issues regarding the adjustment of time durations for the two phases are not discussed here. Readers are referred to [24] for further discussions.

9.2 Routing in Cooperative Networks In previous chapters, we have shown that cooperative transmissions can be used to improve the link quality between the source and the destination with the help of cooperative relays. The cooperative links can be used as the basic building block of end-to-end routes in multihop networks. With improved per-hop link quality, the desired end-to-end performance between the source and the destination can be achieved with less transmission power. However, the problem remains as to how the optimal cooperative route can be found between any source-destination pair. Although conventional routing protocols have been studied extensively over the past years, most of these schemes consider only point-to-point transmission on each hop and, thus, are not directly applicable to cooperative networks. Specifically, with cooperation, each link may involve multiple nodes and the link cost will differ considerably from conventional point-to-point links. Due to these reasons, new routing protocols that take into consideration the characteristics of cooperative transmissions must be developed to fully exploit the advantages of cooperation. In the following, we will first introduce a general formulation of cooperative routing problems and then summarize a few suboptimal but efficient cooperative routing algorithms that can be applied in practice.

9.2.1 General Formulation of Cooperative Routing Consider a multihop network that consists of N nodes, denoted by the set K = {0, 1, . . . , N − 1}. Let s ∈ K be the source node and let d ∈ K − {s} be the destination node. A general formulation of cooperative routing, as proposed in [9], can be viewed as the construction of a sequence of expanding sets, where each set consists of nodes that have reliably decoded the source’s message up to that stage. Specifically, let Sk be the reliable set of stage k, which is defined as the set of nodes that can reliably decode the source’s

374

9 Networking and Cross-Layer Issues in Cooperative Networks Destination

S0

Destination

S1 Source

Source

(a)

(b) Destination

S2

Destination

S3 Source

Source

(c)

(d)

Fig. 9.7 Illustration of the expanding set construction for cooperative routing.

message after k transmission time slots. In the (k + 1)-th time slot, nodes in the set Sk will cooperatively transmit to another set of nodes in the network, say Uk+1 , allowing the reliable set to become Sk+1 = Sk ∪ Uk+1 , where Uk+1 is the set of nodes newly added to the reliably set after the (k + 1)-th time slot. A cooperative routing solution is then a sequence of expanding reliable sets S0 , S1 , . . . , Sk , . . . , ST that starts with S0 = {s} and terminates at state ST if ST is the first to include the destination node, i.e., T = min{k : d ∈ Sk }. Please note that, in general, the transmissions performed at each stage may potentially reach multiple nodes simultaneously and, thus, it is possible to have |Uk | ≥ 1. An illustration of cooperative routing is given in Fig. 9.7. To determine the cooperative route {Sk }Tk=0 (or, equivalently, the sequence of receivers {Uk }Tk=1 ), we must compute, at each stage, the link cost LC(Sk , Uk+1 ) between the set Sk and all possible set of receivers Uk+1 ⊂ K − Sk . This cost depends on the target performance at the receiver(s) and the specific cooperative transmission scheme that is employed. In particular, the link cost can be defined as the minimum transmission energy required to achieve the target SNR or rate at the receiver(s). When perfect

9.2 Routing in Cooperative Networks

375

CSI is available at the transmitters, transmit beamforming can be performed to make the signals arriving at the receiver add up coherently and, thus, requiring significantly less energy to achieve the target SNR compared to the non-cooperative case. When perfect CSI is not available at the transmitter, we can also employ other cooperative transmission schemes such as selective relaying, distributed space-time coding, asynchronous cooperation, etc., as studied in Chapters 3 and 4, which may also provide energy savings under fixed performance requirements. An example of the link cost with respect to transmit beamforming is given in the following. Example (Link Cost of Cooperative Beamforming): Suppose that transmit beamforming is adopted in each stage so that nodes in the set Sk−1 can pre-compensate for the phase rotation imposed by the channel, allowing the signals arriving at the receiver to be of common phase. By assuming that this can be perfectly done, the signal received by node j can be modeled as  yj = |hi,j ||fi |x + w, (9.26) i∈Sk−1

where fi is the beamforming coefficient at node i, x is the transmitted symbol 2 with zero mean and unit variance, w is the AWGN with variance σw , and hij is the channel coefficient between nodes i and j. In this case, the total  transmission energy is given by i |fi |2 and the SNR at the receiver node j is  ( i |hi,j ||fi |)2 SNRj = . 2 σw Let us assume, for simplicity, that there is only one target receiver at each stage (i.e., Uk = {j} for some j ∈ K − Sk−1 ), and that the message can be correctly decoded at the receiver whenever the received SNR exceeds the threshold ρmin . In this case, the beamforming coefficients can be found by solving the optimization problem  min |fi |2 (9.27) {fi ,∀i∈Sk−1 }

subject to

i∈Sk−1

 ( i∈Sk−1 |hi,j ||fi |)2 2 σw

≥ ρmin .

 2 . To Notice that the constraint can be rewritten as i |hi,j ||fi | ≥ ρmin σw solve the optimization problem, we first obtain the Lagrangian function ⎛ ⎞   2 ⎠, |fi |2 + λ ⎝ |hi,j ||fi | − ρmin σw L({fi }) = (9.28) i∈Sk−1

i∈Sk−1

376

9 Networking and Cross-Layer Issues in Cooperative Networks

where λ is the Lagrange multiplier. Then, by setting the derivative of ∂L({fi })/∂|fi | to zero and by choosing λ to satisfy the SNR constraint, we can obtain the optimal beamforming coefficient as fi = 

h∗i,j 2. ρmin σw 2 i |hi,j |

(9.29)

The link cost, which is defined as the sum of the transmission energy over all cooperating transmitters, is then given by 

LC(Sk , j) =

1

|fi |2 = 

i∈Sk−1

i∈Sk−1

|hi,j |2 2 ρmin σw

= 

1

1 i∈Sk−1 LC(i,j)

,

(9.30)

2 /|hi,j |2 is the minimum transmission energy required where LC(i, j) = ρmin σw for i to reach j alone, through direct transmission. In the general case where |Uk | ≥ 1, the beamforming coefficients can be designed so that the worst user’s SNR exceeds ρmin . However, this problem is known to be NP-hard and suboptimal algorithms must be applied. We refer the readers to [17] for further details. 

Given the link costs, the general cooperative routing problem can be modeled as a dynamic programming problem where the state of the system at stage k is represented by the reliable set Sk . The initial state S0 is the set that consists only of the source node s and the terminating states are those that contain the destination node d. Let us define T = {ST ⊂ K : d ∈ ST } as the set of terminating states. The decision variable in the k-th stage is the set Uk ⊂ K − Sk−1 , which is the set of target users for the k-th transmission. The dynamic system is described by the sequence S0 , S1 , . . . , Sk , . . . , ST that evolves as Sk = Sk−1 ∪ Uk , for k = 1, . . . , T. (9.31) The total energy consumption is given by EC =

T  k=1

LC(Sk−1 , Uk ) =

T 

LC(Sk−1 , Sk − Sk−1 ).

(9.32)

k=1

The minimum energy route is found by computing the shortest path from S0 to one of the terminating states T . The state space is represented by the so-called cooperation graph, as shown in Fig. 9.8 for a network of 4 nodes represented by the set K = {s, 1, 2, d}. In the graph, each vertex is represented by a subset of nodes and a transition exists from a state S to another state S  if S ⊂ S  . The link cost corresponding to the transition is LC(S, S  − S). All terminal states in T are then connected to a virtual state D with links of

9.2 Routing in Cooperative Networks

377

S0 = {s} {s, 1}

{s, 1, 2}

{s, 2}

{s, 1, d}

{s, d}

{s, 2, d}

{s, 1, 2, d}

D

virtual links

T¯ = {{s, d}, {s, 1, d}, {s, 2, d}, {s, 1, 2, d}} Fig. 9.8 An example of a cooperation graph.

0 s

1 D

2 D

N −1

3 D

D

D

d

Fig. 9.9 Ilustration of a one-dimensional linear network.

cost 0. However, since the number of states in the cooperation graph increases exponentially with number of users N , the complexity in finding the shortest path is in general O(22N ), which easily becomes intractable as N increases. Hence, suboptimal but efficient algorithms must be derived for practical applications as we describe in the next subsection. Nonetheless, cooperative routing provides significant energy savings compared to the noncooperative case as can be demonstrated by the linear network example given below. Example (Linear Network): To demonstrate the energy savings that can be obtained with cooperation, let us consider a linear network that consists of N nodes equally spaced with distance D on a one-dimensional line, as shown in Fig. 9.9. The nodes are labeled 0 to N − 1 from left to right. We assume that node 0 is the source and node N − 1 is the destination. For simplicity, we also assume that the channel gain between node i and node j is proportional to the distance and remains static over time; therefore, we have |hi,j |2 =

1 , |i − j|α Dα

where α is the path loss exponent. With noncooperative routing, the minimum energy route has each node, say node i − 1, transmit to the closest node in the direction of the destination, i.e., node i, which requires energy 2 LC(i − 1, i) = ρmin σw Dα . Hence, the total energy consumption is equal to

378

9 Networking and Cross-Layer Issues in Cooperative Networks linear EN = C

N −1 

2 LC(i − 1, i) = (N − 1)ρmin σw Dα .

(9.33)

i=1

When transmit beamforming is applied, all nodes that have received the message in previous time slots will cooperate in transmitting to a set of nodes in the downstream of the path. To simplify our analysis, we consider a simplified strategy where, in each time slot, only the closest node in the downstream is reached. In this case, the nodes that can reliably decode the message after k − 1 transmissions is equal to the set Sk−1 = {0, 1, . . . , k − 1}. Then, in the k-th time slot, the minimum energy required to reach the next node on the path (i.e., node k) is 1

LC(Sk−1 , k) = k

1 i=1 LC(i−1,k)

where ζ(k) =

k

1 i=1 iα .

= k

1

1 2 iα D α i=1 ρmin σw

=

2 Dα ρmin σw , ζ(k)

(9.34)

In this case the total energy consumption is equal to

EClinear =

N −1  k=1

LC(Sk−1 , k) =

N −1  k=1

2 ρmin σw Dα . ζ(k)

(9.35)

The energy savings that can be obtained with cooperation is given by Energy Savings =

N −1 linear linear 1  1 EN C − EC . = 1 − linear N −1 ζ(k) EN C k=1

(9.36)

Notice that the limit of ζ(k) as k → ∞ is the Riemann zeta function parameterized by the path loss exponent α. In particular, for α = 2, we have limk→∞ ζ(k) = π 2 /6 and, thus, the energy saving approaches 1 − π62 ≈ 39% [7]. We would like to remark that it is possible to further reduce the total energy expenditure by taking longer hops over the network with cooperative transmissions. Hence, the energy savings indicated above can be viewed as an achievable lower bound to the achievable performance.  As demonstrated in the example above, cooperative routing can significantly reduce the end-to-end energy expenditure in multihop networks. However, the optimal cooperative routing problem is, in general, NP-complete [11], and the cooperative transmissions employed on each hop require additional coordination that is not available in conventional networks. In practice, it is necessary to develop heuristic algorithms that are computationally more tractable and requires coordination that is manageable in practical networks. Some heuristic algorithms are summarized in the following subsection.

9.2 Routing in Cooperative Networks

379

9.2.2 Heuristic Algorithms for Cooperative Routing Most heuristic cooperative routing protocols proposed in the literature take on one of two approaches: (i) the use of conventional noncooperative routing protocols to reduce the complexity of searching for potential receivers on each hop and (ii) the use of cooperative link costs in the execution of well-known shortest path algorithms. We then introduce two routing protocols that essentially construct the shortest route path based on the noncooperative and cooperative link costs, respectively. The cooperation along the minimum energy noncooperative path (CAN) algorithm proposed in [9] exploits the conventional shortest path algorithm to find the optimal noncooperative route path, and then allows the nodes on the route path cooperatively transmit to the next hop in order to save the energy expenditure. Although CAN can effectively reduce the end-to-end cost, it may not fully exploit the advantages of cooperation since optimal cooperative routes can differ substantially from the noncooperative ones. The second approach is to instead allow cooperative routes to be found directly by applying conventional shortest path algorithms on modified link costs that take into consideration the use of cooperative transmissions. A scheme devised using this approach, such as the cooperative shortest path (CSP) algorithm proposed in [11], can potentially find routes that are differ considerably with the noncooperative ones and be closer to the optimal cooperative solution. The schemes mentioned above are summarized in the following. Cooperation Along the Minimum Energy Noncooperative Path (CAN) Algorithm In CAN, the optimal noncooperative route from the source to the destination is first selected. Then, at each hop, the last nodes on the route will transmit cooperatively to the next node. For example, suppose that the noncooperative route is characterized by the sequence of nodes ω = {s, r1 , r2 , . . . , rn , d}. In the first time slot, source s transmits with the minimum energy required to reach r1 . Then, in the next time slot, s and r1 cooperate to transmit to r2 , which yields the cost LC({s, r1 }, r2 ) =

1 |hs,r2 |2 2 ρmin σw

+

|hr1 ,r2 |2 2 ρmin σw

=

1 LC(s,r1 )

1 . + LC(r11 ,r2 )

(9.37)

In the k-th time slot, e.g., for k > , the nodes rk− , . . ., rk−1 cooperatively transmit to node rk which requires cost LC({rk− , . . . , rk−1 }, rk ) = k−1

1

1 i=k− LC(ri ,rk )

.

(9.38)

380

9 Networking and Cross-Layer Issues in Cooperative Networks

The parameter is used to restrict cooperation within a certain number of neighbors since, in practice, it is not reasonable to assume perfect coordination beyond a certain neighborhood. The advantage of CAN is that it requires only the search for the optimal noncooperative route, which requires the complexity of only O(N 2 ), where N is the total number of nodes in the network. Cooperative transmissions is employed in the physical layer without altering the original routing protocol in conventional noncooperative multihop networks and, thus, can be easily adopted in current networking systems. This scheme may lose certain cooperative advantages if the optimal cooperative route differs considerably from the noncooperative ones. Cooperative Shortest Path (CSP) Algorithm Instead of using solutions that are based on conventional noncooperative routes, another approach to finding a cooperative route is to apply wellknown shortest path algorithms, such as Dijkstra’s algorithm [1], on modified link costs that take into consideration the use of cooperative transmissions. Specifically, in CSP, each node maintains two labels: EC (s, i), which represents the total cost (or total energy expenditure) of the cooperative shortest path from source s to node i, and Π(i), which represents the predecessors of node i along the cooperative shortest path. Moreover, let P be the set of nodes whose shortest path from the source s to itself has been determined and let Q be the set whose path is yet to be determined. The CSP algorithm can be described as follows. Initialization: Let s ∈ {0, 1, . . . , N − 1} be the source node. Then, set EC (s, i) = ∞ and Π(i) = ∅ for all i = s, and set EC (s, s) = 0. Also, initialize the sets P and Q as P = ∅ and Q = {0, 1, . . . , N − 1}. While Q is not empty, (i) Find the node with the least total cost, i.e., u := arg min EC (s, i). i∈Q

(ii) Update the sets as P := P ∪ {u} and Q := Q − {u}. (iii) Update the total cost and the predecessor set of each node i ∈ Q by using the relaxation procedure described below. If EC (s, i) > EC (s, u) + LC([Π(u), u], i), then set EC (s, i) := EC (s, u) + LC([Π(u), u], i) and

9.2 Routing in Cooperative Networks

381

50

CAN with =2

Energy Saving (%)

45

40

CSP with =2 CAN with =4 CSP with =4

35

30

25

20 20

25

30

35 40 45 Number of Nodes (N)

50

55

60

Fig. 9.10 Comparison of energy savings for different routing strategies.

Π(i) := [Π(u), u]. Similarly, if cooperation is restricted to the most recently recruited nodes, the link cost should be modified accordingly. The average end-to-end energy savings (that is, one minus the ratio between the energy consumed by utilizing CAN/CSP over that consumed by using non-cooperative shortest path algorithm) with respect to N are compared in Fig. 9.10. Consider a 10 × 10 square region with N nodes randomly located within the region. 50000 scenarios are generated and a source-destination pair is randomly chosen in each scenario. Without loss of generality, here we assume that the noise variance and the minimum required SNR at the receiver are both equal to 1. The channel gain between node i and node j is then de−2 fined as the inverse square of the distance, that is, |hi,j |2 = Di,j , where Di,j is the distance between nodes i and j. Intuitively, the more number of users cooperatively transmit (i.e., the larger ), the lower average energy is required to deliver one packet to the destination. Moreover, by properly determining the route path based on user cooperation (that is, the CSP algorithm), the determined route saves more energy then the route found by non-cooperative shortest path algorithms. Although the schemes described above are effective in reducing the total end-to-end transmission energy, they may have limited application in practice due to the need for centralized control. This motivates the search for decentralized cooperative routing algorithms.

382

9 Networking and Cross-Layer Issues in Cooperative Networks

Distributed Cooperative Routing Algorithm Decentralized cooperative routing solutions are necessary for practical purposes but are difficult to obtain due to the dependence of the link costs on the cooperative path. To find a decentralized solution, one can consider a simplified strategy where transmitters of each hop can recruit cooperative nodes, independent of other links on the path, to serve as relays. In this case, the link costs will be independent for each hop and the shortest path can be found in a decentralized manner by using the distributed Bellman-Ford algorithm [1], as first proposed in [8]. Specifically, let LC(i, R, j) be the minimum total energy needed for node i to reach node j with the help of relays in the set R and let LC(i, ∅, j) be the link cost of direct transmission. Suppose that each node is only able to recruit at most relays for cooperation. Then, the cooperative link cost from node i to node j can be given by LC(i, j) = min LC(i, R, j), R:|R|≤

(9.39)

and the relay set used for the transmission from i to j is Ri,j = arg min LC(i, R, j).

(9.40)

R:|R|≤

By having each node, say node i, compute the link costs LC(i, j) for all j ∈ N (i), where N (i) is the set of nodes in the neighborhood of node i, the cooperative route from the source to the destination can be found by employing the distributed Bellman-Ford algorithm. The distributed cooperative routing algorithm is described as follows. Initialization: Each node i ∈ {1, 2, . . . , N } initializes the cost of reaching the destination d as EC (i, d) = LC(i, d).

(9.41)

Step I: Each node i sends a HELLO message containing the value of EC (i, d) to all reachable nodes in its neighborhood, i.e., N (i). Step II: Update the cost as EC (i, d) = min LC(i, j) + EC (j, d),

(9.42)

j∈N (i)

and find the successor of node i as Ξ(i) = arg min {LC(i, j) + EC (j, d)} . j∈N (i)

(9.43)

9.3 Security Issues in Cooperative Networks

383

Step III: Repeat Steps I and II until the values of EC (i, d) converge separately for each i. Step IV: The sequence of successors starting from the source s to the destination node forms the cooperative route. The cooperative routing protocols described in this section is based on the concept of routing in conventional multihop networks. However, the existence of cooperative communications in the physical layer encourages one to rethink the link abstraction and develop more efficient end-to-end cooperative transmission strategies (let it be a hop-by-hop token passing strategy or an avalanche of signals flooding through the network). An interesting tutorial on this subject can be found in [16].

9.3 Security Issues in Cooperative Networks Throughout this book, we have introduced numerous cooperative strategies from physical to MAC to network layers and have shown the significant performance gains that can be obtained through user cooperation. However, all of these works rely on the assumption that users acting as the relays are fully cooperative and trustworthy. Unfortunately, this may not always be the case in practice. In fact, if the relays do not comply with the cooperative strategy or maliciously transmit garbled messages to interfere with the on-going transmission, the detection performance at the destination may degrade dramatically and the users may be even better off without cooperation. In some cases, the relays may not necessarily be malicious, but just selfishly preserve their own transmission resources for its own use. However, this may still cause user cooperation to breakdown since other users will have no incentive to cooperate. Therefore, it is necessary for the destination to determine the malicious or selfish relays and to take appropriate actions to ensure that cooperative advantages are preserved. This involves the study on security issues in cooperative networks. These issues are mostly discussed from the high-layer perspective, especially in the context of ad hoc networks. However, since most cooperative schemes are closely integrated with physical layer signal processing techniques, cross-layered approaches are necessary to efficiently identify malicious relays and to avoid their attacks.

9.3.1 Misbehavior in Relay Networks In this subsection, we outline some typical types of misbehaviors or attacks in relay networks and introduce possible actions to these unwelcome behaviors. Four types of misbehaviors/attacks are given in the following:

384

9 Networking and Cross-Layer Issues in Cooperative Networks

• Selfish relaying: The case where relays do not expend the required amount of resources to forward the source’s data and preserve the transmission resources for its own transmission. • False information feedback: The case where false information of, e.g., routing path, CSI, or timing etc, are passed on to other users in the network, causing adverse effects on multiuser coordination and signal detection. • Malicious Forwarding: The case where garbled signals or messages are forwarded by the relays in place of the actual source message, causing interference at the destination. • Eavesdropping: The case where users purposely overhear messages that are not intended for themselves and infringe upon the privacy of others. These issues are not new and, in fact, have been studied extensively in the context of multi-hop ad hoc networks, i.e., a special case of cooperative networks. Several strategies have been proposed in the literature to ensure reliable transmission in relay networks containing misbehaving users. For example, to deal with the effect of selfish users, the authors in [21] have modeled the transmission of selfish users as a game and an incentive-based scheduling policy have been proposed to encourage selfish relays to cooperate. In the incentive-based policy, users who are willing to serve as relays receive higher transmission priority so that rational users would choose to cooperate with others in order to enhance its own throughput. For networks that contain malicious relays, the authors in [14] proposed the use of watchdog and pathrater methods to determine a reliable routing path from source to destination, without going through selfish or malicious relays. The selfish or malicious relays are identified by having users monitor the transmission activities of their neighbors (as a watchdog). A selfish user that fails to forward a message or a malicious user that does not transmit the correct packet will be detected by its neighbors (especially the user of its previous hop) and will be marked as a misbehaving user. When a routing request occurs, the pathrater will calculate a cost for each routing path by jointly considering the transmission cost and the reliability of each node along the path. Security issues in ad hoc networks have been studied extensively over the past decade and have resulted in a large field of work. Readers are referred to [18, 23, 26, 27] for further studies on this issue. However, most of the techniques proposed for conventional ad hoc networks require monitoring of the activities of each individual relay and take action in combating single malicious users. However, in cooperative systems, a message is usually retransmitted simultaneously by multiple relays and, thus, no individual behavior of a relay can be examined alone. Even when a transmission failure is detected due to malicious behaviors, it is difficult to identify the malicious relay among the group of cooperating users using purely high-layer methods, as in the examples described above. Using these conventional methods, all relays that are involved in a failed transmission may be seen as misbehaving users, even if it is not due to their misbehavior. The diversity gain will be degraded since many trustworthy users may be excluded from future cooperative transmis-

9.3 Security Issues in Cooperative Networks

(1)

(2)

No

yd , y d

385

MRC Detector

ρ(Θ) < ρmax Revert to Direct Tx

Yes

Destination

Fig. 9.11 Malicious relay detection procedure at the destination.

sions. In the following, we first describe a signal-correlation-based malicious relay detection method for a single relay scenario. Then, a malicious relay discovering procedure will be introduced for networks with multiple relays.

9.3.2 Security in Single-Relay Cooperative Networks Let us first examine the problem of malicious relay detection in a single relay network. Here, we assume perfect CSI at the destination such that the signals received from the source and the relay can be coherently combined to perform the detection. In the single relay case, this detection method can be used along with the high-layer network management policies described in the previous section to ensure security throughout the network. Consider a single-relay network adopting the DF relaying scheme. By considering the two-phase cooperation scheme, the destination will receive two copies of the source signal, one transmitted by the source and the other forwarded by the relay. Intuitively, if the relay conforms to the cooperative strategy, one can expect that the signals transmitted from the source and the relay will be highly correlated. Thus, by exploiting the correlation between both signals, the destination can determine whether or not the relay is misbehaving [4, 5]. A flow diagram of the detection procedure at the destination is illustrated in Fig. 9.11, with details provided in the following. Suppose that the source transmits a BPSK symbol xs [m] with transmission power E[|xs [m]|2 ] = Ps in the m-th symbol period. The signal received at the destination in Phase I can be written as (1)

(1)

yd [m] = hs,d [m]xs [m] + wd [m],

for m = 0, . . . , M − 1,

(9.44) (1)

where hs,d [m] is the channel coefficient on the s-d link and wd [m] ∼ 2 CN (0, σw ) is the AWGN at the destination. To focus on the effects of malicious relay behavior, we assume that the relay is always able to decode the

386

9 Networking and Cross-Layer Issues in Cooperative Networks

data symbols correctly and that it is semi-malicious, meaning that it transmits a garbled signal only with a certain probability and acts cooperatively, otherwise. This is a general model that includes the case where relays are faulty, causing it to be prone to errors, or when it is try to avoid being identified by the source or destination. Let us denote the signal forwarded by the relay as Θ[m]xs [m], where Θ[m] is a random variable that is used to capture the relay behavior. By assuming that Θ[m] takes on q possible values, i.e., θ1 , θ2 , · · · , θq , the probability density function (PDF) of Θ[m] can be written as fΘ (θ) = p1 δ(θ − θ1 ) + p2 δ(θ − θ2 ) + · · · + pq δ(θ − θq ).

(9.45)

Without loss of generality, we can assume that |Θ[m]| ≤ 1. For example, we can consider three special cases: (i) Θ[m] = 1, which indicates that the relay conforms with the cooperative strategy and retransmits exactly the source’s symbol; (ii) 0 ≤ |Θ[m]|  1, which indicates that the relay forwards symbols with a small power, and (iii) Θ[m] = −1, which indicates that the relay transmits opposite symbols to interfere with the detection at the destination. The signal received at the destination in Phase II can be written as (2)

(2)

yd [m] = hr,d [m]Θ[m]xs [m] + wd [m],

(9.46)

for m = 0, . . . , M − 1. By multiplying the signals with their MRC coefficients, the signals we obtain in both phases can be expressed as (1)

h∗s,d [m] (1) h∗s,d [m] (1) yd [m] = |hs,d [m]|xs [m] + w [m] |hs,d [m]| |hs,d [m]| d

y¯d [m] =

h∗r,d [m] (2) h∗r,d [m] (2) yd [m] = |hr,d [m]|Θ[m]xs [m] + w [m], |hr,d [m]| |hr,d [m]| d

y¯d [m] = and (2)

respectively. The destination can then evaluate the correlation of the signals by J  (2) (1) H= 1 R y¯ [mj ](¯ y d [mj ])∗ , (9.47) J j=1 d where m1 , . . ., mJ can be a set of symbol periods chosen across different time frames. The correlation is then normalized by estimates of the signal variances, e.g., J  (1) I2 = 1 σ |¯ y [mj ]|2 1 J j=1 d

J  (2) I2 = 1 and σ |¯ y [mj ]|2 , 2 J j=1 d

to obtain the estimated correlation coefficient

9.3 Security Issues in Cooperative Networks

H R ρH(Θ) =   . I2 σ I2 σ 1 2

387

(9.48)

The value of ρH(Θ) is then utilized to perform the malicious relay detection at the destination. ˆ will converge to the value When J is sufficiently large, R E[¯ yd (¯ yd )∗ ] = E[|hs,d |]E[|hr,d |]E[Θ]Ps . (2)

(1)

(9.49)

If we further assume that the s-d and r-d channels are both i.i.d. Rayleigh distributed with E[|hs,d |2 ] = E[|hr,d |2 ] = 1, the correlation in (9.49) will become π (2) (1) ∗ E[¯ yd (¯ yd ) ] = E[Θ]Ps . (9.50) 4 Hence, as J increases, the estimate ρH(Θ) will approach the value (2) (1) ∗ π yd ) ] E[¯ yd (¯ 4 E[Θ]Ps = . ρ(Θ) =  2 2) (2) 2 (1) 2 (Ps + σw )(Ps E[|Θ|2 ] + σw E[|¯ yd | ]E[|¯ yd | ]

(9.51)

It is observed from (9.51) that the case with Pr(Θ = 1) = 1 (i.e., the case where the relay is fully cooperative) will yield the maximum correlation coefficient π Ps ρmax  ρ(1) = 4 2 . (9.52) Ps + σw Therefore, the destination can examine the cooperative status of the relay by comparing the estimated correlation coefficient with the value of ρmax and then determine the cooperation mode that the network should follow. The operations are summarized in the block diagram illustrated in Fig. 9.11. Note that, to evaluate the symbol correlation more accurately, the destination may need to collect multiple symbols for a period of time and then calculate the average correlation of the received symbols. This procedure can be easily generalized to the case with multiple relays, especially when relays are assumed to transmit over orthogonal channels. In Fig. 9.12, the correlation coefficient ρ(Θ) is shown with respect to the SNR for the case where the relay is fully cooperative (i.e., “cooperating relay” curve) and three other cases where relays demonstrate semi-malicious relay behaviors. In Case I, the relay introduces an amplitude distortion with probability pa such that the received SNR at the destination is reduced. In Case II, the relay imposes a random phase rotation with probability pp . In Case III, with probability pa + pp , both phase and amplitude distortions are introduced. As observed from Fig. 9.12, the phase distortion results in a significant reduction in the correlation between the signals received from the source and the relay. This is due to the fact that the received signal may be canceled out if the relay reproduces an opposite symbol (i.e., −xs [m]).

388

9 Networking and Cross-Layer Issues in Cooperative Networks

Fig. 9.12 Correlation between received signals. (From Dehnie, Sencar and Memon. c 2007 IEEE.)

9.3.3 Security in Multi-Relay Cooperative Networks In this subsection, we consider the problem of malicious relay detection in multi-relay transmissions. The problem is particularly challenging in this case since the signals received at the destination is a mixture of signals forwarded by all relays, including both cooperative and malicious relays. The separation of relay signals can be performed using ML detection, but the detection may be unreliable (even for signals transmitted by cooperative relays) since the signals transmitted by malicious relays may cause severe interference at the destination. The operations are described below. Let us consider a multi-relay network where the source’s messages are forwarded over a single channel using non-orthogonal cooperative transmission schemes such as those introduced in Chapters 4.2–4.4. In this case, the signals forwarded by the relays will be superimposed at the receiver, making it difficult to identify the signal of each relay. Nonetheless, ML detection can be used in an attempt to separate the signals, as shown in [12, 13], and used to evaluate the signal correlation between each relay and the source. Instead of computing the correlation between the source and the relay symbols, we assume in this case that the source signal is negligible at the destination and perform the correlation test with a sequence of tracing symbols that are known a priori at both the source and the destination. The tracing symbols are assumed to be generated by a pseudo-random number generator (PRNG) based on a certain tracing key, known at both the source and the destina-

9.3 Security Issues in Cooperative Networks

389

6RXUFHVLGH SURFHVVLQJ

'HVWLQDWLRQVLGH SURFHVVLQJ

Fig. 9.13 Procedure of detecting malicious relays using tracing symbols. (From Mao and c Wu; [2007] IEEE ).

tion (but concealed from the relays), and randomly inserted into the data stream transmitted by the source. By calculating the correlation of the tracing symbols forwarded by each relay with their corresponding correct ones, the destination can identify the malicious relays. The procedure is schematically illustrated in Fig. 9.13 (see also [12, 13] for details). More specifically, suppose that the two-phase cooperative scheme is adopted and that all relays are able to successfully decode the source message in Phase I. The malicious relay detection procedure at the destination can be summarized as follows. Step I: Detect the tracing symbols forwarded by each relay and calculate the correct detection probability during these symbol periods. Specifically, let pc [m] be the correct detection probability in the m-th symbol period. Step II: Remove the detected symbols with correct detection probability pc [m] less than a certain threshold τ . That is, only the detected symbols that are sufficiently reliable will be utilized to perform the correlation test. Step III: Compute the normalized signal correlation ρ , for = 1, . . . , L, between the actual tracing symbols and the detected symbols corresponding to relay . Relay is considered cooperative if ρ ≥ η and is considered malicious, otherwise. Once a relay is determined to be malicious, it will be excluded from the cooperative set in future transmissions. An example is given in the following. Example (Two-Relay Cooperative Network): In the following let us first consider a DF cooperative network with two relays. By adopting the two-phase cooperation scheme, the two relays forward x1 ∈ M and x2 ∈ M, respectively in Phase II, where the set M is the set containing all possible modulated symbol points in the constellation. Assume that the destination is equipped with D antennas, and the signal received at

390

9 Networking and Cross-Layer Issues in Cooperative Networks

Fig. 9.14 The combined signal constellations U with constellation of transmitted signal M = {x0 , x1 , x2 , x3 }, and the channel coefficients h1,d = 1/2 and h2,d = ejπ/4 . (From c Mao and Wu. 2007 IEEE.)

the destination is given by yd = h1,d x1 + h2,d x2 + wd ,

(9.53)

where {hi,d }i=1,2 are the D × 1 vectors of channel coefficients from the i-th relay to the D receive antennas at the destination. Assume that the destination has perfect knowledge of the channel coefficients h1,d and h2,d . By observing (9.53), we can see that, even though the signals x1 and x2 come from the constellation M, the actual signal received at the destination will instead belong to the combined signal constellation given by U = {u : u = h1,d x1 + h2,d x2 , x1 , x2 ∈ M}. When h1,d = h2,d (which occurs with probability 1 under continuous fading models), each signal point in U will correspond to a unique vector of symbols forwarded by the two relays, i.e., (x1 , x2 ), and the cardinality is given by |U| = |M|2 . For instance, consider the case where QPSK symbols, i.e., M = {x0 , x1 , x2 , x3 }, are transmitted by the source and that the destination is equipped with only a single antenna. Let h1,d = [1/2] and h2,d = [ejπ/4 ], the set U contains 16 combined signal constellations, which are illustrated as the dots in Fig. 9.14 (provided in [12, 13]). The symbols forwarded by the relays, i.e., xr = (x1 , x2 ), can be detected by using ML detection, which suffices to find the corresponding combined signal constellation closest to yd . ˆ d ∈ U be the combined symbol vector detected at the destination. Let x Since the combined signal constellation is not symmetric, the probability that ˆ d = uk depends on the an error occurs when the detected symbol vector is x distance between uk and other combined signal points in U. Let xd be the actual combined signal vector received at the destination. Then, the error probability can be expressed as

9.3 Security Issues in Cooperative Networks

391

 Pr(e|ˆ xd = uk ) =

xd = uk |xd = ui ) Pr(xd = ui ) ui ∈U ,i=k Pr(ˆ  , xd = uk |xd = ui ) Pr(xd = ui ) ui ∈U Pr(ˆ

(9.54)

where Pr(ˆ xd = uk |xd = ui ) is the probability that uk is detected when the actual combined signal is ui and Pr(xd = ui ) is the probability that ui is the actual combined signal vector. Assume that each constellation point u ∈ U was sent with equal prior probability. By omitting the possible error to other constellation points, we can approximate Pr(ˆ xd = uk |xd = ui ) ≈ Pr(ˆ xd = ui |xd = uk ). The error probability can then be approximated as  Pr(ˆ xd = ui |xd = uk ). (9.55) Pr(e|ˆ xd = uk ) ≈ ui ∈U ,i=k

The error probability in (9.55) can be estimated numerically using Monte Carlo method. Moreover, one should note that the error probability can be effectively reduced if the number of receive antennas is increased. By employing the aforementioned detection scheme and by utilizing the approximate error probability expression, the tracing procedure at the destination can be described as follows: Step I Use ML detector to detect the tracing symbols forwarded by the two relays. Step II Remove those detected symbols whose detection probability pc [m] = 1 − Pr(e|ˆ xd [m] = uk ) is less than a threshold τ . Step III Suppose that the sequence of tracing symbols corresponding to relay is mapped into the binary sequence [t [m1 ], t [m2 ], . . . , t [mJ ]] according to a predetermined bit assignment following the Gray code, and that the corresponding sequence of symbols detected at the destination is mapped into the binary sequence [s [m1 ], s [m2 ], . . . , s [mJ ]], where both t [m] and s [m] are antipodal, i.e., they take on values in {−1, +1}. Compute the normalized correlation between the tracing symbols and the detected symbols as J ρ =   I

j=1 s [mj ]t [mj ]

2 j=1 s [mj ]

 J

.

(9.56)

2 j=1 t [mj ]

Identify relay as malicious if ρ < η and identify it as cooperative, otherwise. 

9 Networking and Cross-Layer Issues in Cooperative Networks

1XPEHURIDSSHDUDQFHV

392

Fig. 9.15 Histogram of ρ under a basic attack with perfect channel state information. c (From Mao and Wu with modified labels. 2007 IEEE.)

The malicious relay detection scheme can be easily generalized to the case with more than two relays [13]. When there are L relays, the signal received at the destination can be represented as yd =

L 

h,d x + wd .

(9.57)

=1

Let xr = (x1 , x2 , · · · , xL ) be the vector of symbols transmitted by the L ˆ r be the corresponding symbols detected at the destination. relays and let x By applying ML detection, the estimated relay symbols is given by ˆ r = arg min yd − x xr

∈ML

L 

h,d x 2 .

(9.58)

=1

In this case, the combined signal set U contains |M|L points and the error probability of the detected symbols can be evaluated by (9.55). The tracing procedure then follows similarly. It is worthwhile to note that the computation of the correlation estimate is a key step in identifying malicious relays. However, this requires a certain delay overhead in order to collect enough data so that a reliable correlation estimate can be computed. Based on the correlation estimate, the malicious relay detection problem can be modeled as a binary hypothesis testing where hypothesis H0 represents the case that the relay is cooperative and hypothesis

393

1XPEHURIDSSHDUDQFHV

9.3 Security Issues in Cooperative Networks



Fig. 9.16 Histogram of ρ under random attack with perfect channel state information. c (From Mao and Wu with modified labels. 2007 IEEE.)

H1 represents the case that it is adversarial. Details on the statistics of the correlation estimate under different relay attack models can be found in [13]. The distribution of the symbol correlation can be measured with random experiments. The histogram of ρ under SNR = 14 dB in a two-relay network is illustrated in Fig. 9.15. In this experiment, one random relay behaves maliciously, while the other is fully cooperative. One can observe that the average of ρ under H0 (i.e., when the relay is cooperative), denoted by μρ|H0 , is approximately 1 and the average under H1 (i.e., when the relay is adversarial) is μρ|H1 = 0. In this case, it is reasonable to set the decision threshold as η = (μρ|H0 + μρ|H1 )/2. When the variances of ρ under the two hypotheses are small, the tracing procedure described above provides a reliable detection of malicious relays. Moreover, in practice, the relay may randomize its behavior to avoid being caught by the destination. In this case, the adversarial relay can garble the relayed symbols with probability q and transmit the correct symbol with probability 1 − q. When q = 1/4, the histogram of ρ under SNR = 12 dB is illustrated in Fig. 9.16. In this case, we can see that the difference between the expectations of ρ under the two hypotheses becomes smaller and, thus, it is more difficult to identify the adversarial relays. Apart from detecting the adversarial relays at the destination, one may adopt more aggressive strategies, such as payment-based or reputation-based methods, to stimulate cooperation from selfish or malicious relays. Readers are referred to [15, 25] for further details.

394

9 Networking and Cross-Layer Issues in Cooperative Networks

References 1. Bertsekas, D., Gallager, R.: Data Networks, 2nd edn. Prentice Hall (1992) 2. Chang, C.-S.: Stability, queue length, and delay of deterministic and stochastic queueing networks. IEEE Transactions on Automatic Control 39(5), 913–931 (1994) 3. Chang, C.-S., Thomas, J.A.: Effective bandwidth in high-speed digital networks. IEEE Journal on Selected Areas in Communications 13(6), 1091–1100 (1995) 4. Dehnie, S., Sencar, H., Memon, N.: Detecting malicious behavior in cooperative diversity. In: Proceedings of the Conference on Information Science and Systems (CISS), pp. 895–899 (2007) 5. Dehnie, S., Sencar, H.T., Memon, N.: Cooperative diversity in the presence of misbehaving relay: Performance analysis. In: IEEE Sarnoff Symposium (2007) 6. Goel, S., Negi, R.: Guaranteeing secrecy using artificial noise. IEEE Transactions on Wireless Communications 7(6), 2180 –2189 (2008) 7. Hong, Y.-W., Scaglione, A.: Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks. IEEE Transactions on Wireless Communications 5(10), 2844–2855 (2006) 8. Ibrahim, A.S., Han, Z., Liu, K.J.R.: Distributed energy-efficient cooperative routing in wireless networks. IEEE Transactions on Wireless Communications 7(10), 3930–3941 (2008) 9. Khandani, A.E., Abounadi, J., Modiano, E., Zheng, L.: Cooperative routing in static wireless networks. IEEE Transactions on Communications 55(11), 2185–2192 (2007) 10. Laneman, J.N., Tse, D.N.C., Wornell, G.W.: Cooperative diversity in wireless networks: Efficient protocols and outage behavior. IEEE Transactions on Information Theory 50(12), 3062–3080 (2004) 11. Li, F., Wu, K., Lippman, A.: Minimum energy cooperative path routing in all-wireless networks: NP-completeness and heuristic algorithms. Journal of Communications and Networks 10(2), 204-212 (2008) 12. Mao, Y., Wu, M.: Security issues in cooperative communications: Tracing adversarial relays. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. IV–69–IV–72 (2006) 13. Mao, Y., Wu, M.: Tracing malicious relays in cooperative wireless communications. IEEE Transactions on Information Forensics and Security 2(2), 198–212 (2007) 14. Marti, S., Giuli, T., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile ad hoc networks. In: Proceedings of ACM MobiCom, pp. 255–265 (2000) 15. Michiardi, P., Molva, R.: CORE: A collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks. In: Proceedings of The 6th IFIP Conference on Communications and Multimedia Security, pp. 107 – 121 (2002) 16. Scaglione, A., Goeckel, D.L., Laneman, J.N.: Cooperative communications in mobile ad-hoc networks: Rethinking the link abstraction. IEEE Signal Processing Magazine 23(15), 18–29 (2006) 17. Sidiropoulos, N.D., Davidson, T.D., Luo, Z.Q.: Transmit beamforming for physicallayer multicasting. IEEE Transactions on Signal Processing 54(6), 2239–2251 (2006) 18. Sun, B., Osborne, L., Xiao, Y., Guizani, S.: Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wireless Communications 14(5), 56–63 (2007) 19. Tang, J., Zhang, X.: Cross-layer resource allocation over wireless relay networks for quality of service provisioning. IEEE Journal on Selected Areas in Communications 25(4), 645–656 (2007) 20. Tang, J., Zhang, X.: Quality-of-service driven power and rate adaptation for multichannel communications over wireless links. IEEE Transactions on Wireless Communications 6(12), 4349–4360 (2007) 21. Wei, H.-Y., Gitlin, R.D.: Incentive mechanism design for selfish hybrid wireless relay networks. Mobile Networks and Applications 10(6), 929–937 (2005)

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Index

Symbols 3rd Generation Partnership Project (3GPP) 7 A ad hoc networks 383–385 Alamouti space-time code 39–41, 160 amplify-and-forward (AF) 2, 9 basic AF 87–99 optimum power allocation 91–92, 95–97 outage probability 90–92, 94–99 CDMA 242, 244–246, 248 channel estimation 168–173 distributed space-time code (DSTC) 161–168 diversity-multiplexing tradeoff 208– 209, 220 fixed-gain AF relaying scheme 97–99 incremental AF 87, 100–102 multi-hop transmissions 182–187 nonorthogonal amplify-and-forward (NAF) 208 orthogonal cooperation 127–133, 138 optimum power allocation 130–132 outage probability 128, 129, 131 SDMA 255 selective relaying 140, 141, 147–150 space-frequency code (SFC) 301–303 TDMA 233, 235 transmit beamforming 135–141, 144, 149 antenna selection 4 asynchronous cooperative transmissions 9, 125, 187–190

Automatic Retransmission reQuest (ARQ) 344 B backpressure algorithm 347 base station (BS) 7 beamforming 4, 9, 125, 135–146, 153, 248, 251–254 amplify-and-forward (AF) 135–141, 149 decode-and-forward (DF) 141–146, 151 link cost 375 Bellman-Ford algorithm 382 Bernoulli process 319 binary exponential backoff 335 block Markov coding 197 broadcast channels (BC) 6 broadcast cut see cut-set bound, 215 broadcast cut-set bound 215 buffer overflow 363 C capacity compress-and-forward 202 decode-and-forward 197 Gaussian degraded relay channel 198 Gaussian relay channel 196, 197, 200, 202 multi-relay networks 213–219 carrier sensing multiple access with collision avoidance (CSMA/CA) 10, 333 cave-filling 177, 231 CDMA see code-division multiple access channel estimation 9, 125

Y.-W. Peter Hong et al., Cooperative Communications and Networking, DOI 10.1007/978-1-4419-7194-4, © Springer Science+Business Media, LLC 2010

397

398 AF multi-relay systems 168–173 DF multi-relay systems 173–177 LMMSE estimator 170, 174 MMSE estimator 170, 174 single-relay systems 115–120 channel inversion 364 Cholesky decomposition 249 Claim For Cooperation (CFC) 344 Clear Channel Assessment (CCA) 334 Clear-To-Send (CTS) 334 code-division multiple access (CDMA) 9, 227, 237–254 coded cooperation (CC) 2, 9, 67, 102–113, 264 space-time coded cooperation (STCC) 113 user multiplexing for 106–113 coherence bandwidth 22, 24 coherence time 22, 23 collision channel 318 collision resolution 329 compress-and-forward (CF) 2, 9, 67, 114–115, 201–203 capacity 201 contention window 335 cooperation along the minimum energy noncooperative path (CAN) algorithm 379 cooperation graph 377 cooperative diversity 299 cooperative maximum differential backlog (CMDB) policy 354 cooperative OFDM systems distributed space-frequency code (DSFC) 292–303 Lee-Zhang-Xia construction 295 Seddik-Liu construction 299 mutli-relay systems frequency-domain beamforming 284 selective relaying 289 time-domain beamforming 286 pair-wise cooperative systems power allocation for full-repetition systems 274 subcarrier matching 278 selective relaying subcarrier-based selective relaying in OFDM systems 290 symbol-based selective relaying in OFDM systems 289 selective relaying with subcarrier matching 290 subcarrier matching 274, 278

Index ordered subcarrier pairing (OSP) 281, 291 random subcarrier matching (RSM) 281 RF-based subcarrier matching 279 cooperative routing 5, 6, 10, 361, 373–383 distributed 382–383 energy consumption 376 energy saving 377 link cost 373 beamfroming 375 cooperative shortest path (CSP) algorithm 380 CoopMAC 336–343 Coordinated Multiple Point (CoMP) 8 Coordinated Scheduling/Beamforming (CS/CB) 8 cut-set bound 195, 204, 214 broadcast cut 196, 215 broadcast cut-set bound 215 max-flow min-cut 196 multi-relay system 214 multiple access cut 196, 215 multiple-access cut-set bound 216 cyclic prefix (CP) 272 cyclic redundancy check (CRC) 107, 133, 141, 188, 293, 300, 344 D DCF interframe space (DIFS) 334 decode-and-forward (DF) 2, 9, 318 basic DF 68–79, 81, 82, 91, 95, 96 optimum power allocation 70–72, 74, 77–78 outage probability 70–72, 75–78 capacity 197 CDMA 239, 242–245, 248 channel estimation 173–177 demodulate-and-forward (DeF) 68, 81–87 suboptimal cooperative MRC (C-MRC) detector 85–87 distributed space-time code (DSTC) 153–161 diversity-multiplexing tradeoff 212, 221 dynamic decode-and-forward (DDF) 211 multi-hop transmissions 179–182 orthogonal cooperation 133–134 partner selection 260 selection DF 68, 77–81 selective relaying 134, 150–153 space-frequency code (SFC) 293–301

Index TDMA 230, 232 transmit beamforming 141–146, 151 with error detection at relays 141–142 without error detection at relays 141–145 decorrelating receiver 331 degraded relay channel 197 reversely degraded relay channel 200 delay spread 274 determinant criterion 294 Dijkstra’s algorithm 380 discrete Fourier transform (DFT) DFT matrix 272 discrete memoryless relay channel 194 discrete-time baseband model 22 distributed coordination function (DCF) 333 distributed space-frequency code (DSFC) 292–303 distributed space-time code (DSTC) 4, 9, 125, 153–169, 252, 253 amplify-and-forward (AF) 161–168 decode-and-forward (DF) 153–161 diversity cooperative diversity 299 multipath diversity 299 diversity combining 27–34 diversity gain 1 multipath diversity gain 301 of cooperative OFDM systems using space-frequency codes 294, 295 diversity-multiplexing tradeoff (DMT) 9, 15, 58–63, 207–213, 220–221 multi-relay networks 220–221 single-relay AF systems 208 single-relay DF systems 211 dominant system 321 Doppler effect 23 down-convert 20 dynamic decode-and-forward (DDF) 211 diversity-multiplexing tradeoff 212, 221 dynamic programming 376 E eavesdropping 384 effective bandwidth 361, 362 effective capacity 361–363 effective capacity region 372 eNodeB 7 equal-gain combining (EGC) 27 ergodic capacity definition of 49 multi-relay fading channels 220

399 single-relay fading channels

203–206

F fading fast fading 203 flat fading 24 frequency selective fading 24 Nakagami fading 25, 185 phase fading 205, 220 quasi-static 207 Rayleigh fading 25 Rician fading 25 slow fading 203 fast fading 203 FDMA see frequency-division multiple access frame checking sequence (FCS) 340 free space model see path loss frequency-division multiple access (FDMA) 9, 227, 229, 234, 254 Frobenius norm 331 full repetition (FR) scheme 275 full-duplex 195 fully-loaded region 323 fully-loaded system 322 G Gaussian relay channel 193–203 degraded channel 198 multi-relay channel 213–214 single-relay channel 193–195 Global System for Mobile Communications (GSM) 20 H half-duplex 195 handshaking 334 Helper-ready-To-Send (HTS)

337

I IEEE 802.11 333 frame format 340 IEEE 802.11a 271, 281 IEEE 802.16 (WiMAX) 271 IEEE 802.16e 7 IEEE 802.16j 6 inband relay 7 inverse discrete Fourier transform (IDFT) 272 J Jensen’s inequality

51, 205

400 Joint Processing (JP)

Index 8

K Karush-Kuhn-Tucker (KKT) conditions 56, 57, 130, 131, 175, 231, 233 KKT conditions see Karush-KuhnTucker (KKT) conditions L large deviations 362, 363 line-of-sight (LOS) 16, 17, 25 linear dispersion (LD) space-time code 161, 168, 169 linear minimum mean square error (LMMSE) estimator 170, 174 Long Term Evolution (LTE) 271 Long Term Evolution (LTE)-Advanced 6–8 Loynes’ theorem 322 M malicious relay 361, 383–387, 389, 392, 393 matched filter (MF) 240, 243, 245 matched filter bank (MFB) 240, 241, 243–245, 247–250 matrix inversion lemma 307 max-flow min-cut 196 maximal-ratio combining (MRC) 30 maximum differential backlog (MDB) policy 347 maximum likelihood (ML) 241, 330 maximum likelihood (ML) decoding 40–43, 157, 162, 166, 167, 294, 302 mean square error (MSE) distortion measure 216 medium access control (MAC) 4, 6, 10, 317, 361 MIMO relays 305–314 minimum mean square error (MMSE) 258 multiuser detector 241–246, 248, 249, 253 SDMA 256 MMSE see minimum mean square error mobile station (MS) 7 multi-hop transmissions 5, 9, 125, 178–187 amplify-and-forward (AF) 182–187 decode-and-forward (DF) 179–182, 185 multicarrier systems 10

multipath diversity 26, 299 multipath fading 1, 2, 15, 19–25, 49, 188–190, 237, 272, 281, 288, 298 multiple access channels (MAC) 6 multiple access cut see cut-set bound, 215 multiple access interference (MAI) 4, 227, 237–241, 246, 249, 252, 253, 256, 257 multiple relay systems diversity-multiplexing tradeoff 220, 221 multiple-access cut-set bound 216 multiple-input multiple-output (MIMO) 15 multiple-input multiple-output (MIMO) system 1, 10, 44, 254 diversity order of 47 parallel decomposition of channel 46 multiple-input single-output (MISO) system 35, 216 antenna selection 38 capacity of 216 space-time code 39 transmit beamforming 36 multiplexing gain 4 multiterminal source coding 6 multiuser detection (MUD) 237–253 blind MMSE-MUD 246 decorrelating MUD 241, 249 MMSE-MUD 241–246, 248, 249, 253 relay-assisted decorrelating MUD (RAD-MUD) 249–253 N Nakagami fading 25 near-far effect 237, 240 Network Allocation Vector (NAV) 334 network capacity region 321 network-assisted diversity multiple access (NDMA) 329 non-line-of-sight (NLOS) 24 non-regenerative relaying see amplifyand-forward (AF) nonorthogonal amplify-and-forward (NAF) 208 diversity-multiplexing tradeoff 209, 220 nontransparent relay 7 O opportunistic relaying see selective relaying opportunistic scheduling 229, 234–237

Index

401

orthogonal frequency-division multiplexing (OFDM) 10, 271–274 orthogonal space-time block code (OSTBC) 154, 155, 158, 160, 161 outage 2 outage capacity definition of 50 outage probability definition of 50 outband relay 7

relay channel 5–6, 9 with feedback 199 relay stations (RS) 5, 7 resource allocation 1, 4, 9, 274–282 reversely degraded relay channel 200 Rician fading 25 RMS delay spread 281, 288 round-robin scheduling 229–234 routing 5, 10, 373–383, see also cooperative routing

P

S

pairwise error probability (PEP) 43, 157, 162–165 of AF single-relay system 209 of AF space-frequency codes 301 of DF space-frequency codes 294 partner selection centralized 260–267 decentralized 267–268 path loss 1 definition of 15–16 free space 16 general model 17 two-ray model 16 pathrather 384 Persistent Relay Carrier Sensing Multiple Access (PRCSMA) 344 phase fading 205, 220 Physical Layer Convergence Procedure (PLCP) 336 point coordination function (PCF) 334 pseudo-random number generator (PRNG) 388

saturated system 341 saturated throughput 343 scheduling 4, 7, 8, 10 SDMA see space-division multiple access security 383–393 ad hoc networks 383–385 multi-relay networks 388–393 single-relay networks 385–387 selection combining (SC) 29 selective relaying 2, 9, 125, 146–153, 248, 251–253, 259, 260 amplify-and-forward (AF) 140, 147–150 decode-and-forward (DF) 134, 150–153 OFDM systems with multiple relays 288–292 selfish relay 384 separation theorem 216 shadowing 1, 18–19, 49 short interframe space (SIFS) 334 shortest path algorithm 379 single-input multiple-output (SIMO) system 26–34, 215 capacity of 215 diversity order of 30, 34 slotted ALOHA 10, 317–332 slow fading 203 space-division multiple access (SDMA) 9, 227, 254–259 space-frequency code (SFC) 1, 10 amplify-and-forward (AF) 301–303 decode-and-forward (DF) 293–301 distributed space-frequency code (DSFC) 292–303 space-time block code (STBC) 155, 158, 160, 161 space-time code (STC) 1, 7, 39 Alamouti code 39–41 differential 157 distributed see distributed space-time code (DSTC)

Q QoS exponent 363 quality-of-service (QoS) 6, 10, 361–373 quasi-static channel 207 queue stability 320 R random access 4 rank criterion 294 rate-distortion function 216 with mean square error (MSE) distortion measure 216 Rayleigh fading 25 Ready-To-Send (RTS) 334 regenerative relaying see decode-andforward (DF)

402 linear dispersion (LD) 161, 166, 168, 169 orthogonal space-time block code (OSTBC) 42, 154, 155, 158, 160, 161 space-time block code (STBC) 39, 41, 42, 154, 155 space-time trellis code (STTC) 41, 160 spatial diversity 2, 4, 6, 26 sphere decoding 162 stability region 321 stable throughput 318, 321 subcarrier matching 274, 278, 290 subscriber station (SS) 7 T TDMA see time-division multiple access time-division multiple access (TDMA) 9, 227–237, 254 transmit beamforming see beamforming transparent relay 7 two-ray model see path loss

Index Type 1 relay

8

U up-convert 20 uplink 229, 238–253 User Equipment (UE)

7

W watchdog 384 water-filling 50, 57, 231, 277, 308, 312 WiMAX see Worldwide Interoperability for Microwave Access wireless local area networks (WLAN) 333 Worldwide Interoperability for Microwave Access (WiMAX) 7–8 Z zero-forcing (ZF)

242, 249, 257–259