Standards for 5G and beyond will require communication systems with a much more flexible and cognitive design to support

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*English*
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[681]*
*Year 2020*

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- Hüseyin Arslan (editor)
- Ertuğrul Başar (editor)

*Table of contents : CoverContentsAbout the editorsForewordList of acronymsPart I. Waveform design: an overview 1 Introduction to waveform design 1.1 Introduction 1.2 The generalized definition of a waveform 1.3 Relationships of channel and RF impairments with a waveform 1.4 Application requirements of cellular use cases and wireless fidelity (Wi-Fi) standards 1.4.1 Cellular communications use cases 1.4.2 Wi-Fi communications standards 1.5 Impact of the waveform design on RATs 1.5.1 Limitations and challenges for RATs 1.5.2 Performance indicators for the waveform design 1.5.3 Waveform design guidelines for RATs 1.6 An example of waveform frame: 5G NR standardization 1.6.1 Reference documents for 3GPP 1.6.2 Numerology structures 1.6.3 Bandwidth part issues 1.6.4 Slot structures 1.6.5 Comparison for building blocks of 5G NR and LTE 1.7 Conclusion References 2 OFDM and alternative waveforms 2.1 Introduction 2.2 The baseline for waveform discussion: CP-OFDM 2.2.1 Key features 2.2.2 Performance in multipath channel 2.2.2.1 Time-dispersive multipath channel 2.2.2.2 Frequency-dispersive multipath channel 2.2.3 Performance with impairments 2.2.3.1 Frequency offset 2.2.3.2 Symbol timing error 2.2.3.3 Sampling clock offset 2.2.3.4 Phase noise 2.2.3.5 PA nonlinearities 2.2.3.6 IQ impairments 2.3 Alternative waveforms 2.3.1 Multicarrier schemes 2.3.1.1 Windowed-orthogonal frequency division multiplexing 2.3.1.2 Filter bank multicarrier 2.3.1.3 Generalized frequency division multiplexing 2.3.1.4 Universal filtered multicarrier 2.3.1.5 Filtered-orthogonal frequency division multiplexing 2.3.2 Single-carrier schemes 2.3.2.1 CP-DFT-s-OFDM 2.3.2.2 ZT-DFT-s-OFDM 2.3.2.3 UW-DFT-s-OFDM 2.4 Discussion 2.5 Conclusion References 3 Mixed numerology OFDM and interference issues 3.1 Introduction 3.2 Mixed numerology multiplexing 3.2.1 Frequency domain 3.2.2 Time domain 3.3 Inter-numerology interference modeling 3.4 Factors affecting INI 3.4.1 Subcarrier spacing ratio, Q 3.4.2 Power offset 3.4.3 Channel response 3.5 INI management 3.5.1 Restructuring INI through common CP 3.5.1.1 INI analysis with common CP 3.5.2 INI-aware scheduling 3.5.3 INI-aware guard band allocation 3.6 Asynchronicity in the mixed numerology frame 3.7 Mixed numerology in single-carrier schemes 3.8 Summary ReferencesPart II. Flexible waveform and modulation options for beyond 5G 4 Flexibility through hybrid waveforms 4.1 Introduction 4.2 Improved OFDM-based flexible structures for beyond 5G applications 4.2.1 Spectrally localized OFDM 4.2.1.1 Adaptive symbol transitioned OFDM 4.2.1.2 Precoded OFDM 4.2.1.3 Partial transmit sequenced OFDM 4.2.1.4 OFDM with alignment signals 4.2.2 Secure OFDM 4.2.3 Beyond spectral localization: partially overlapping waveforms 4.3 Waveform multiplexing approaches for beyond 5G RATs 4.3.1 Time-domain OFDM numerology multiplexing 4.3.2 FDM of OFDM numerologies against hybrid waveforms 4.4 Numerology-based scheduling 4.5 Conclusion Acknowledgment References 5 Generalized and flexible modulation options 5.1 Introduction 5.2 The relation between modulation and waveform in communication systems 5.3 Flexibility in modulation design 5.4 Classifications of the modulation options for 5G and beyond waveforms 5.4.1 Conventional and differential digital modulations for OFDM-based waveform 5.4.2 Multi-dimensional modulation options for OFDM-based waveform 5.5 Index-based modulation options 5.5.1 SM-OFDM scheme 5.5.2 OFDM-IM scheme 5.6 Number-based modulation options 5.7 Shape-based modulation options 5.8 Performance evaluation and comparison of modulation options in practical conditions 5.8.1 Spectral efficiency 5.8.2 Reliability 5.8.3 PAPR and power efficiency 5.8.4 Out-of-band leakage 5.8.5 Computational complexity 5.9 Applications of the featured modulation options for 5G and beyond networks 5.10 Other potential flexible modulation options for OFDM-based waveforms 5.11 Futuristic modulation options for beyond 5G 5.12 Conclusion References 6 Index modulation-based flexible waveform design 6.1 Introduction 6.2 Index modulation in frequency domain: OFDM with index modulation 6.2.1 Maximum likelihood detector 6.2.2 Log-likelihood ratio detector 6.3 State-of-the-art OFDM-IM solutions 6.3.1 Interleaved OFDM-IM 6.3.2 Generalized OFDM-IM 6.3.3 Dual-mode OFDM 6.3.4 Coordinate interleaved OFDM-IM 6.4 Flexible OFDM with IM 6.4.1 Subcarrier mapping scheme 6.4.1.1 Equal bit protection 6.4.1.2 Robustness against asynchronous transmission 6.4.1.3 Avoiding deep fading 6.4.2 Subcarrier activation ratio 6.4.2.1 Avoiding Doppler spread 6.4.2.2 Robustness against hardware imperfection 6.4.2.3 Securing communication link 6.5 Discussions and future directions 6.6 Conclusion Acknowledgment ReferencesPart III. Multiple antenna systems for 5G and beyond 7 Massive MIMO for 5G and beyond 7.1 Introduction of massive MIMO 7.2 Information theory of massive MIMO 7.2.1 Fundamental of massive MIMO 7.2.2 Spectrum efficiency analysis of massive MIMO 7.2.2.1 Perfect CSI 7.2.2.2 Imperfect CSI 7.3 Channel models for massive MIMO 7.3.1 Correlation-based channel model 7.3.2 Spatial channel model 7.4 Signal detection for massive MIMO 7.4.1 System model and MMSE detection 7.4.2 Neumann sequence-based signal detection 7.4.3 Iteration-based signal detection 7.5 CSI acquisition for massive MIMO 7.5.1 Channel estimation for massive MIMO 7.5.1.1 Channel sparsity-based channel estimation 7.5.1.2 Channel correlation-based channel estimation 7.5.2 Channel feedback for massive MIMO 7.5.2.1 Channel sparsity-based channel feedback 7.5.2.2 Channel correlation-based channel feedback 7.5.2.3 Channel partial reciprocity-based channel feedback 7.6 Precoding for massive MIMO 7.6.1 Digital precoding 7.6.1.1 Single-user digital precoding 7.6.1.2 Multiuser digital precoding 7.6.2 Analog beamforming 7.6.3 Hybrid precoding 7.6.3.1 Single-user hybrid precoding 7.6.3.2 Multiuser hybrid precoding 7.7 Prototype and testbeds for massive MIMO 7.8 Challenges and future research directions for massive MIMO 7.8.1 Physical layer signal processing in wideband massive MIMO 7.8.2 THz massive MIMO 7.8.3 RIS-based massive MIMO 7.9 Summary of the key points for massive MIMO References 8 Beamforming and beam management in 5G and beyond 8.1 Introduction 8.2 Evolution of beamforming 8.3 Beamforming in mmWave frequencies 8.3.1 Analog/digital beamforming 8.3.1.1 Analog beamforming 8.3.1.2 Digital beamforming 8.3.2 Hybrid beamforming 8.3.2.1 Fully connected hybrid beamforming 8.3.2.2 Sub-connected hybrid beamforming 8.3.3 Beampattern adaptation 8.3.4 Lens antenna for beamforming 8.4 Beam management 8.4.1 Beam management classes 8.4.1.1 Non-standalone architecture 8.4.1.2 Standalone architecture 8.4.2 Beam switching 8.4.3 Beam tracking 8.4.4 Security-oriented beamforming techniques 8.5 Challenges and future concepts 8.5.1 Pilot contamination in mmWave frequencies 8.5.2 Multi-lens antenna beamforming systems 8.5.3 IRS-based beamforming 8.6 Conclusion References 9 Spatial modulation techniques for beyond 5G 9.1 Basic principle and variants of SM 9.1.1 Single-RF SM 9.1.2 Generalized SM 9.1.3 Differential SM 9.1.4 Receive SM 9.2 Performance enhancement for SM 9.2.1 Link-adaptive SM 9.2.2 Precoding/TCM-aided SM 9.2.3 Transmit-diversity-enhanced SM 9.3 Generalized SM integration with other promising technologies 9.3.1 Compressed-sensing (CS) theory for SM 9.3.2 Non-orthogonal multiple access (NOMA)-aided SM 9.3.3 Security provisioning in SM 9.4 Applications of SM to emerging communication systems 9.4.1 SM in mmWave communications 9.4.2 SM in optical wireless communications 9.4.3 SM-based simultaneous wireless information and power transfer 9.4.4 SM-based molecular communication 9.5 Conclusions References 10 Beyond massive MIMO: reconfigurable intelligent surface-assisted wireless communications 10.1 Introduction 10.2 Controllable wireless propagation: two illustrative examples 10.2.1 Two-ray propagation with RISs 10.2.2 Eliminating Doppler effects with RISs 10.3 A brief literature survey 10.4 Potential use-cases 10.5 Conclusions and future perspectives Acknowledgment ReferencesPart IV. Channel modeling and new frequency bands 11 Channel modeling for 5G and beyond 11.1 Introduction 11.1.1 What defines a good channel model for 5G and B5G? 11.2 Evolution of radio frequency channel models before 5G 11.2.1 Analytical channel models 11.2.1.1 Correlation-based models 11.2.1.2 Propagation-motivated models 11.2.2 Physical channel models 11.2.2.1 Geometry-based stochastic models 11.2.2.2 Non-geometry-based stochastic models 11.2.2.3 Deterministic models 11.2.3 Standardized channel models 11.2.3.1 The COST channel models (259 and 273) 11.2.3.2 The multidimensional parametric channel model 11.2.3.3 The 3GPP spatial channel model 11.2.3.4 The WINNER channel model 11.2.3.5 IMT-advanced channel models from ITU 11.3 Channel models for 5G and beyond 11.3.1 Enhanced 3GPP channel models 11.3.2 The MiWEBA channel model 11.3.3 METIS channel models 11.3.3.1 The METIS map-based model 11.3.3.2 The METIS stochastic model 11.3.3.3 The METIS hybrid model 11.3.4 The QuaDRiGa/mmMAGIC channel model 11.3.5 The IEEE 802.11ay channel model 11.3.6 The IMT-2020 channel model 11.3.7 The NYUSIM channel model 11.4 Machine learning-based channel modeling for 5G and B5G 11.5 Channel sparsity and compressed modeling in 5G and B5G 11.5.1 Pilot reduction through compressive channel sampling 11.5.2 Channel sparsity aspects in 5G and B5G 11.5.3 Outstanding challenges and questions 11.6 Conclusion Acknowledgment References 12 On the advances of terahertz communication for 5G and beyond wireless networks 12.1 Introduction 12.2 Application scenarios 12.2.1 Fronthaul and backhaul links 12.2.2 Nano devices 12.2.3 Entertainment technologies and augmented reality 12.2.4 Heterogeneous networks 12.3 Challenges and solutions 12.3.1 Transceivers design in terahertz band 12.3.1.1 Amplifiers 12.3.2 Channel and noise modeling 12.3.2.1 Channel 12.3.2.2 Molecular absorption noise and loss 12.3.3 Physical layer 12.3.3.1 Modulation schemes 12.3.3.2 Channel codes 12.3.3.3 MIMO systems 12.3.3.4 Medium access control 12.3.3.5 Synchronization 12.4 Achieved data rates 12.5 Modeling the wireless propagation channel for terahertz band: a case study for 240–300 GHz 12.5.1 Description of measurement setup 12.5.1.1 Measurement methodology 12.5.2 Measurement results 12.6 Conclusion and future directions References 13 Visible light communication for 5G and beyond 13.1 Introduction 13.2 Standardization activities 13.3 System design 13.3.1 Channel modeling 13.3.1.1 Indoor light propagation 13.3.1.2 LOS and NLOS channel models 13.3.1.3 Channel parameters 13.3.2 Optical modulation schemes 13.3.2.1 Carrierless modulation schemes 13.3.2.2 Single-carrier modulation schemes 13.3.2.3 Multi-carrier modulation schemes 13.3.2.4 Multicolor modulation schemes 13.3.3 Medium access control 13.4 Integrated visible light communication systems 13.4.1 Integration of IR and VLC 13.4.2 Integration of RF and VLC 13.4.3 Integration of PLC and VLC 13.4.4 Integration of VLC in 5G networks 13.5 Applications ofVLC in 5G and beyond 13.5.1 Indoor 13.5.1.1 Hospitals 13.5.1.2 Industries 13.5.1.3 Data centers 13.5.1.4 Secure communication 13.5.2 Outdoor 13.5.3 Underwater 13.5.4 Underground 13.6 Summary ReferencesPart V. Coexistence, interference and radio resource management 14 Coordinated networks: past, present and future 14.1 Coordination in legacy networks 14.1.1 Frequency reuse 14.1.2 Intercell interference coordination 14.1.3 Enhanced intercell interference coordination 14.1.4 CoMP and its essentials 14.1.4.1 CoMP architecture 14.1.4.2 CoMP types 14.1.4.3 CoMP scenarios 14.1.5 CoMP implementation 14.1.5.1 User selection and resource allocation 14.1.5.2 Clustering 14.1.5.3 Reference signals and interference measurement 14.2 Coordination in 5G networks 14.2.1 Throughput 14.2.2 Reliability and latency 14.2.3 Coverage 14.2.4 Mobility 14.2.5 Spectral efficiency 14.2.6 Energy efficiency 14.3 Coordination for future wireless networks 14.3.1 Network architecture 14.3.1.1 Cloud-based radio access network 14.3.1.2 Fog-RAN 14.3.2 Smart radio environment 14.3.3 Communication technologies and standards 14.3.4 Application and user requirements 14.4 Challenges for future coordinated networks 14.4.1 Synchronization/timing advance 14.4.2 Functionality split 14.4.3 Backhaul issues 14.4.4 Performance analysis 14.5 Conclusion Acknowledgments References 15 Non-orthogonal radio access technologies 15.1 Introduction 15.2 Non-orthogonal multiple accessing in power domain 15.2.1 Downlink PD-NOMA 15.2.2 Uplink PD-NOMA 15.2.3 Capacity in PD-NOMA 15.2.4 Fairness in PD-NOMA 15.3 State-of-the-art NOMA solutions 15.3.1 Low-density spreading orthogonal frequency division multiple access 15.3.2 Pattern division multiple access 15.3.3 Index modulation in NOMA 15.4 Grant-free random access techniques 15.4.1 Transmission schemes 15.4.1.1 Reactive transmission 15.4.1.2 K-Repetitions 15.4.1.3 Proactive transmission 15.4.2 Adaptive resource utilization 15.5 Waveform coexistence for multiple accessing 15.5.1 Wideband and narrowband signals 15.5.2 OFDM with OFDM-IM 15.5.3 OFDM with multi-numerology 15.6 Discussions and future directions 15.7 Conclusion and remarks Acknowledgment References 16 Cognitive radio spectrum sensing: from conventional approaches to machine-learning-based predictive techniques 16.1 Introduction 16.2 A brief description of cognitive radio concept 16.2.1 Spectrum sensing 16.2.2 Spectrum decision 16.2.3 Spectrum sharing 16.2.4 Spectrum mobility 16.3 Traditional spectrum-sensing techniques 16.3.1 Narrowband spectrum sensing 16.3.1.1 Methodologies 16.3.1.2 Limitations 16.3.2 Wideband spectrum sensing 16.3.2.1 Methodologies 16.3.2.2 Limitations 16.4 Predictive spectrum-sensing approach 16.4.1 Employed machine-learning methodologies 16.4.1.1 Hidden Markov models 16.4.1.2 Artificial neural networks 16.4.1.3 Deep learning 16.4.2 State-of-the-art 16.5 QoS-aware dynamic spectrum access techniques 16.5.1 Performance evaluation 16.6 Conclusion References 17 Deep learning and federate learning toward 6G mobile communications 17.1 Introduction to machine learning and deep learning 17.2 Deep learning for wireless communication systems and networks 17.2.1 Artificial neural network basics 17.2.2 Data-driven prediction using deep learning 17.2.2.1 Taxi trajectory data set 17.2.2.2 Meteorology data set 17.2.2.3 Points of interest data set 17.2.2.4 Spatial prediction of trip demand 17.2.2.5 Temporal prediction and multivariate long and short term memory model 17.2.2.6 TheAE model 17.2.2.7 The MLSTM model 17.2.2.8 Method to prevent overfitting 17.2.3 Deep learning for signal detection in digital communication systems 17.2.4 Future network architect of machine learning 17.2.4.1 Machine learning in mobile communication networks 17.2.4.2 Networked multi-agent systems 17.3 Federate learning over wireless communications 17.3.1 Federated learning basics 17.3.2 Federated learning through wireless communications 17.3.3 Federated learning over wireless networks 17.3.4 Federated learning over multiple access communications 17.4 Spectrum map in cognitive radio networks by statistical inference and learning 17.5 Conclusions ReferencesPart VI. Securing wireless communication 18 Physical layer security designs for 5G and beyond 18.1 Introduction and motivation 18.2 Fundamentals, preliminaries, and basic system model for PLS 18.3 Secrecy notions and performance metrics 18.3.1 Secrecy notions 18.3.1.1 Perfect secrecy 18.3.1.2 Strong secrecy 18.3.1.3 Weak secrecy 18.3.1.4 Semantic secrecy 18.3.1.5 Ideal secrecy 18.3.2 Secrecy performance metrics 18.4 Popular security techniques 18.4.1 PLS based on secure channel coding design 18.4.1.1 Concepts, merits, and demerits 18.4.1.2 Learned lessons 18.4.2 Channel-based adaptation and optimization for PLS 18.4.2.1 Concepts, merits, and demerits 18.4.2.2 Examples in time, frequency, and space domains 18.4.2.3 Learned lessons 18.4.3 Addition of artificially interfering (noise/jamming) signals for PLS 18.4.3.1 Concepts, merits, and demerits 18.4.3.2 Examples in time, frequency, and space domains 18.4.3.3 Learned lessons 18.4.4 Extraction of secret sequences from wireless channels 18.4.4.1 Concepts, merits, and demerits 18.4.4.2 Examples in time, frequency, and space 18.4.4.3 Learned lessons 18.5 Applications of PLS in emerging technologies 18.5.1 PLS in mmWave 18.5.2 PLS in mMIMO 18.5.3 PLS in URLLC 18.5.4 PLS in IoT 18.5.5 PLS in UAV 18.5.6 PLS in CR systems 18.6 PHY-authentication against spoofing attacks 18.6.1 Channel-based PHY-authentication 18.6.2 AFE-based PHY-authentication 18.6.3 Reliability of PHY-authentication algorithms 18.6.4 Efficient and fast authentication in complex heterogeneous networks 18.6.5 Integration with the existing network infrastructure and authentication protocols 18.7 Wireless jamming attacks and countermeasures 18.7.1 Wireless jamming attacks: a brief summary 18.7.2 Wireless jamming attacks, detection, and solutions 18.7.2.1 Constant jammer 18.7.2.2 Intermittent jammer 18.7.2.3 Reactive jammer 18.7.2.4 Adaptive jammer 18.7.2.5 Intelligent jammer 18.8 Challenges and future research directions 18.8.1 Secrecy design based on service requirements 18.8.2 Cross-layer security design 18.8.3 PAPR of AN-based and precoding security techniques 18.8.4 Security in LOS environment 18.8.5 Robust channel estimation and channel reciprocity calibration 18.8.6 Joint design of secrecy, throughput, delay, and reliability 18.8.7 Hybrid security techniques 18.8.8 Impersonation attacks 18.8.9 Challenges related to solution against jamming attacks 18.8.10 Mixed attacks in wireless networks and cognitive security 18.8.11 A new direction for PLS 18.9 Conclusion Acknowledgments References 19 Physical layer security for NOMA systems 19.1 Introduction 19.2 Fundamentals of NOMA 19.2.1 Downlink NOMA 19.2.2 Uplink NOMA 19.3 Fundamentals of PLS 19.3.1 Information-theoretic secrecy 19.3.2 Metrics 19.3.2.1 Ergodic secrecy capacity 19.3.2.2 Secrecy outage probability 19.4 PLS-enhanced NOMA 19.4.1 PLS in SISO–NOMA systems 19.4.2 PLS in MIMO–NOMA systems 19.4.3 PLS in massive MIMO–NOMA systems 19.5 Conclusion ReferencesIndexBack Cover*

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Flexible and Cognitive Radio Access Technologies for 5G and Beyond

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Flexible and Cognitive Radio Access Technologies for 5G and Beyond Edited by Hüseyin Arslan and Ertug˘rul Bas¸ ar

The Institution of Engineering and Technology

Published by The Institution of Engineering and Technology, London, United Kingdom The Institution of Engineering and Technology is registered as a Charity in England & Wales (no. 211014) and Scotland (no. SC038698). © The Institution of Engineering and Technology 2020 First published 2020 This publication is copyright under the Berne Convention and the Universal Copyright Convention. All rights reserved. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may be reproduced, stored or transmitted, in any form or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publisher at the undermentioned address: The Institution of Engineering and Technology Michael Faraday House Six Hills Way, Stevenage Herts, SG1 2AY, United Kingdom www.theiet.org While the authors and publisher believe that the information and guidance given in this work are correct, all parties must rely upon their own skill and judgement when making use of them. Neither the authors nor publisher assumes any liability to anyone for any loss or damage caused by any error or omission in the work, whether such an error or omission is the result of negligence or any other cause. Any and all such liability is disclaimed. The moral rights of the authors to be identiﬁed as authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

British Library Cataloguing in Publication Data A catalogue record for this product is available from the British Library

ISBN 978-1-83953-079-1 (hardback) ISBN 978-1-83953-080-7 (PDF)

Typeset in India by MPS Limited Printed in the UK by CPI Group (UK) Ltd, Croydon

Contents

About the editors Foreword List of acronyms

xix xxi xxix

Part I Waveform design: an overview

1

1 Introduction to waveform design Ahmet Yazar and Hüseyin Arslan

3

1.1 1.2 1.3 1.4

Introduction The generalized definition of a waveform Relationships of channel and RF impairments with a waveform Application requirements of cellular use cases and wireless fidelity (Wi-Fi) standards 1.4.1 Cellular communications use cases 1.4.2 Wi-Fi communications standards 1.5 Impact of the waveform design on RATs 1.5.1 Limitations and challenges for RATs 1.5.2 Performance indicators for the waveform design 1.5.3 Waveform design guidelines for RATs 1.6 An example of waveform frame: 5G NR standardization 1.6.1 Reference documents for 3GPP 1.6.2 Numerology structures 1.6.3 Bandwidth part issues 1.6.4 Slot structures 1.6.5 Comparison for building blocks of 5G NR and LTE 1.7 Conclusion References 2 OFDM and alternative waveforms Ali Fatih Demir and Hüseyin Arslan 2.1 Introduction 2.2 The baseline for waveform discussion: CP-OFDM 2.2.1 Key features 2.2.2 Performance in multipath channel 2.2.3 Performance with impairments 2.3 Alternative waveforms 2.3.1 Multicarrier schemes

3 4 6 8 8 10 11 11 12 14 15 16 17 19 20 21 23 24 29 29 31 31 35 40 52 52

viii

Flexible and cognitive radio access technologies for 5G and beyond 2.3.2 Single-carrier schemes 2.4 Discussion 2.5 Conclusion References

3 Mixed numerology OFDM and interference issues Abuu B. Kihero, Muhammad Sohaib J. Solaija, and Hüseyin Arslan 3.1 Introduction 3.2 Mixed numerology multiplexing 3.2.1 Frequency domain 3.2.2 Time domain 3.3 Inter-numerology interference modeling 3.4 Factors affecting INI 3.4.1 Subcarrier spacing ratio, Q 3.4.2 Power offset 3.4.3 Channel response 3.5 INI management 3.5.1 Restructuring INI through common CP 3.5.2 INI-aware scheduling 3.5.3 INI-aware guard band allocation 3.6 Asynchronicity in the mixed numerology frame 3.7 Mixed numerology in single-carrier schemes 3.8 Summary References

59 62 63 64 67 67 70 70 71 73 76 76 77 80 80 80 86 88 91 92 92 94

Part II Flexible waveform and modulation options for beyond 5G

97

4 Flexibility through hybrid waveforms Berker Peköz, Selçuk Köse, and Hüseyin Arslan

99

4.1 Introduction 4.2 Improved OFDM-based flexible structures for beyond 5G applications 4.2.1 Spectrally localized OFDM 4.2.2 Secure OFDM 4.2.3 Beyond spectral localization: partially overlapping waveforms 4.3 Waveform multiplexing approaches for beyond 5G RATs 4.3.1 Time-domain OFDM numerology multiplexing 4.3.2 FDM of OFDM numerologies against hybrid waveforms 4.4 Numerology-based scheduling 4.5 Conclusion Acknowledgment References

99 101 101 112 117 120 120 122 126 135 135 136

Contents 5 Generalized and flexible modulation options Ahmad M. Jaradat, Jehad M. Hamamreh, and Hüseyin Arslan 5.1 Introduction 5.2 The relation between modulation and waveform in communication systems 5.3 Flexibility in modulation design 5.4 Classifications of the modulation options for 5G and beyond waveforms 5.4.1 Conventional and differential digital modulations for OFDM-based waveform 5.4.2 Multi-dimensional modulation options for OFDM-based waveform 5.5 Index-based modulation options 5.5.1 SM-OFDM scheme 5.5.2 OFDM-IM scheme 5.6 Number-based modulation options 5.7 Shape-based modulation options 5.8 Performance evaluation and comparison of modulation options in practical conditions 5.8.1 Spectral efficiency 5.8.2 Reliability 5.8.3 PAPR and power efficiency 5.8.4 Out-of-band leakage 5.8.5 Computational complexity 5.9 Applications of the featured modulation options for 5G and beyond networks 5.10 Other potential flexible modulation options for OFDM-based waveforms 5.11 Futuristic modulation options for beyond 5G 5.12 Conclusion References 6 Index modulation-based flexible waveform design Seda Tusha, Armed Tusha, Ertu˘grul Ba¸sar, and Hüseyin Arslan 6.1 Introduction 6.2 Index modulation in frequency domain: OFDM with index modulation 6.2.1 Maximum likelihood detector 6.2.2 Log-likelihood ratio detector 6.3 State-of-the-art OFDM-IM solutions 6.3.1 Interleaved OFDM-IM 6.3.2 Generalized OFDM-IM 6.3.3 Dual-mode OFDM 6.3.4 Coordinate interleaved OFDM-IM

ix 143 143 145 146 147 149 150 151 152 154 155 157 159 159 161 162 164 164 165 166 169 170 171 175 175 176 180 180 180 181 181 182 183

x Flexible and cognitive radio access technologies for 5G and beyond 6.4 Flexible OFDM with IM 6.4.1 Subcarrier mapping scheme 6.4.2 Subcarrier activation ratio 6.5 Discussions and future directions 6.6 Conclusion Acknowledgment References Part III

184 185 189 191 192 192 193

Multiple antenna systems for 5G and beyond

195

7 Massive MIMO for 5G and beyond Jingbo Tan, XiuhongWei, Shuangkaisheng Bi, Mingyao Cui, and Linglong Dai

197

7.1 Introduction of massive MIMO 7.2 Information theory of massive MIMO 7.2.1 Fundamental of massive MIMO 7.2.2 Spectrum efficiency analysis of massive MIMO 7.3 Channel models for massive MIMO 7.3.1 Correlation-based channel model 7.3.2 Spatial channel model 7.4 Signal detection for massive MIMO 7.4.1 System model and MMSE detection 7.4.2 Neumann sequence-based signal detection 7.4.3 Iteration-based signal detection 7.5 CSI acquisition for massive MIMO 7.5.1 Channel estimation for massive MIMO 7.5.2 Channel feedback for massive MIMO 7.6 Precoding for massive MIMO 7.6.1 Digital precoding 7.6.2 Analog beamforming 7.6.3 Hybrid precoding 7.7 Prototype and testbeds for massive MIMO 7.8 Challenges and future research directions for massive MIMO 7.8.1 Physical layer signal processing in wideband massive MIMO 7.8.2 THz massive MIMO 7.8.3 RIS-based massive MIMO 7.9 Summary of the key points for massive MIMO References 8 Beamforming and beam management in 5G and beyond Liza Afeef and Hüseyin Arslan 8.1 Introduction 8.2 Evolution of beamforming 8.3 Beamforming in mmWave frequencies 8.3.1 Analog/digital beamforming

197 198 198 201 205 205 205 207 207 209 211 213 214 222 230 231 233 234 241 242 242 243 243 244 245 249 249 250 253 254

Contents 8.3.2 Hybrid beamforming 8.3.3 Beampattern adaptation 8.3.4 Lens antenna for beamforming 8.4 Beam management 8.4.1 Beam management classes 8.4.2 Beam switching 8.4.3 Beam tracking 8.4.4 Security-oriented beamforming techniques 8.5 Challenges and future concepts 8.5.1 Pilot contamination in mmWave frequencies 8.5.2 Multi-lens antenna beamforming systems 8.5.3 IRS-based beamforming 8.6 Conclusion References 9 Spatial modulation techniques for beyond 5G Miaowen Wen 9.1 Basic principle and variants of SM 9.1.1 Single-RF SM 9.1.2 Generalized SM 9.1.3 Differential SM 9.1.4 Receive SM 9.2 Performance enhancement for SM 9.2.1 Link-adaptive SM 9.2.2 Precoding/TCM-aided SM 9.2.3 Transmit-diversity-enhanced SM 9.3 Generalized SM integration with other promising technologies 9.3.1 Compressed-sensing (CS) theory for SM 9.3.2 Non-orthogonal multiple access (NOMA)-aided SM 9.3.3 Security provisioning in SM 9.4 Applications of SM to emerging communication systems 9.4.1 SM in mmWave communications 9.4.2 SM in optical wireless communications 9.4.3 SM-based simultaneous wireless information and power transfer 9.4.4 SM-based molecular communication 9.5 Conclusions References

xi 256 260 261 263 263 270 271 273 274 275 276 277 277 277 283 283 283 285 288 291 292 292 292 293 294 294 295 296 298 298 299 301 302 303 303

10 Beyond massive MIMO: reconfigurable intelligent surface-assisted wireless communications Ertu˘grul Ba¸sar

317

10.1 Introduction 10.2 Controllable wireless propagation: two illustrative examples

317 320

xii

Flexible and cognitive radio access technologies for 5G and beyond 10.2.1 Two-ray propagation with RISs 10.2.2 Eliminating Doppler effects with RISs 10.3 A brief literature survey 10.4 Potential use-cases 10.5 Conclusions and future perspectives Acknowledgment References

Part IV

Channel modeling and new frequency bands

11 Channel modeling for 5G and beyond Mahmoud Nazzal, Mehmet A. Aygül, and Hüseyin Arslan

320 323 326 330 331 332 332 339 341

11.1 Introduction 11.1.1 What defines a good channel model for 5G and B5G? 11.2 Evolution of radio frequency channel models before 5G 11.2.1 Analytical channel models 11.2.2 Physical channel models 11.2.3 Standardized channel models 11.3 Channel models for 5G and beyond 11.3.1 Enhanced 3GPP channel models 11.3.2 The MiWEBA channel model 11.3.3 METIS channel models 11.3.4 The QuaDRiGa/mmMAGIC channel model 11.3.5 The IEEE 802.11ay channel model 11.3.6 The IMT-2020 channel model 11.3.7 The NYUSIM channel model 11.4 Machine learning-based channel modeling for 5G and B5G 11.5 Channel sparsity and compressed modeling in 5G and B5G 11.5.1 Pilot reduction through compressive channel sampling 11.5.2 Channel sparsity aspects in 5G and B5G 11.5.3 Outstanding challenges and questions 11.6 Conclusion Acknowledgment References

341 343 348 349 353 359 362 363 363 363 365 365 366 367 367 370 371 372 373 374 374 374

12 On the advances of terahertz communication for 5G and beyond wireless networks Kür¸sat Tekbıyık, Ali Rıza Ekti, Ali Görçin, and Güne¸s Karabulut Kurt

379

12.1 Introduction 12.2 Application scenarios 12.2.1 Fronthaul and backhaul links 12.2.2 Nano devices 12.2.3 Entertainment technologies and augmented reality 12.2.4 Heterogeneous networks

379 381 381 382 383 383

Contents 12.3 Challenges and solutions 12.3.1 Transceivers design in terahertz band 12.3.2 Channel and noise modeling 12.3.3 Physical layer 12.4 Achieved data rates 12.5 Modeling the wireless propagation channel for terahertz band: a case study for 240–300 GHz 12.5.1 Description of measurement setup 12.5.2 Measurement results 12.6 Conclusion and future directions References 13 Visible light communication for 5G and beyond Muhammad Bilal Janjua and Hüseyin Arslan

xiii 383 384 386 388 391 392 392 394 398 399 403

13.1 Introduction 13.2 Standardization activities 13.3 System design 13.3.1 Channel modeling 13.3.2 Optical modulation schemes 13.3.3 Medium access control 13.4 Integrated visible light communication systems 13.4.1 Integration of IR and VLC 13.4.2 Integration of RF and VLC 13.4.3 Integration of PLC and VLC 13.4.4 Integration of VLC in 5G networks 13.5 Applications of VLC in 5G and beyond 13.5.1 Indoor 13.5.2 Outdoor 13.5.3 Underwater 13.5.4 Underground 13.6 Summary References

403 406 406 409 413 419 420 421 421 421 421 422 423 427 428 428 429 429

Part V Coexistence, interference and radio resource management

433

14 Coordinated networks: past, present and future Muhammad Sohaib J. Solaija, Abuu B. Kihero, and Hüseyin Arslan 14.1 Coordination in legacy networks 14.1.1 Frequency reuse 14.1.2 Intercell interference coordination 14.1.3 Enhanced intercell interference coordination 14.1.4 CoMP and its essentials 14.1.5 CoMP implementation

435 435 436 437 438 438 442

xiv

Flexible and cognitive radio access technologies for 5G and beyond 14.2 Coordination in 5G Networks 14.2.1 Throughput 14.2.2 Reliability and latency 14.2.3 Coverage 14.2.4 Mobility 14.2.5 Spectral efficiency 14.2.6 Energy efficiency 14.3 Coordination for future wireless networks 14.3.1 Network architecture 14.3.2 Smart radio environment 14.3.3 Communication technologies and standards 14.3.4 Application and user requirements 14.4 Challenges for future coordinated networks 14.4.1 Synchronization/timing advance 14.4.2 Functionality split 14.4.3 Backhaul issues 14.4.4 Performance analysis 14.5 Conclusion Acknowledgment References

15 Non-orthogonal radio access technologies Armed Tusha, Seda Tusha, and Hüseyin Arslan 15.1 Introduction 15.2 Non-orthogonal multiple accessing in power domain 15.2.1 Downlink PD-NOMA 15.2.2 Uplink PD-NOMA 15.2.3 Capacity in PD-NOMA 15.2.4 Fairness in PD-NOMA 15.3 State-of-the-art NOMA solutions 15.3.1 Low-density spreading orthogonal frequency division multiple access 15.3.2 Pattern division multiple access 15.3.3 Index modulation in NOMA 15.4 Grant-free random access techniques 15.4.1 Transmission schemes 15.4.2 Adaptive resource utilization 15.5 Waveform coexistence for multiple accessing 15.5.1 Wideband and narrowband signals 15.5.2 OFDM with OFDM-IM 15.5.3 OFDM with multi-numerology 15.6 Discussions and future directions 15.7 Conclusion and remarks Acknowledgment References

444 445 445 446 446 447 447 448 448 451 452 454 455 455 455 456 456 456 457 457 461 461 462 462 463 464 466 467 467 468 469 470 470 472 473 473 474 476 477 478 478 478

Contents 16 Cognitive radio spectrum sensing: from conventional approaches to machine-learning-based predictive techniques Metin Ozturk, Attai Ibrahim Abubakar, Sajjad Hussain, Qammer H. Abbasi, and Muhammad Ali Imran 16.1 Introduction 16.2 A brief description of cognitive radio concept 16.2.1 Spectrum sensing 16.2.2 Spectrum decision 16.2.3 Spectrum sharing 16.2.4 Spectrum mobility 16.3 Traditional spectrum-sensing techniques 16.3.1 Narrowband spectrum sensing 16.3.2 Wideband spectrum sensing 16.4 Predictive spectrum-sensing approach 16.4.1 Employed machine-learning methodologies 16.4.2 State-of-the-art 16.5 QoS-aware dynamic spectrum access techniques 16.5.1 Performance evaluation 16.6 Conclusion References 17 Deep learning and federate learning toward 6G mobile communications Kwang-Cheng Chen, Chun-Hung Liu, Ismail Uysal, Chunxiao Jiang, and Qimei Cui 17.1 Introduction to machine learning and deep learning 17.2 Deep learning for wireless communication systems and networks 17.2.1 Artificial neural network basics 17.2.2 Data-driven prediction using deep learning 17.2.3 Deep learning for signal detection in digital communication systems 17.2.4 Future network architect of machine learning 17.3 Federate learning over wireless communications 17.3.1 Federated learning basics 17.3.2 Federated learning through wireless communications 17.3.3 Federated learning over wireless networks 17.3.4 Federated learning over multiple access communications 17.4 Spectrum map in cognitive radio networks by statistical inference and learning 17.5 Conclusions References

xv

481

481 484 485 485 486 486 486 486 488 492 493 495 498 502 505 505

513

513 516 516 520 526 528 531 531 533 534 536 537 538 538

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Flexible and cognitive radio access technologies for 5G and beyond

Part VI

Securing wireless communication

18 Physical layer security designs for 5G and beyond Haji M. Furqan, Jehad M. Hamamreh and Hüseyin Arslan 18.1 Introduction and motivation 18.2 Fundamentals, preliminaries, and basic system model for PLS 18.3 Secrecy notions and performance metrics 18.3.1 Secrecy notions 18.3.2 Secrecy performance metrics 18.4 Popular security techniques 18.4.1 PLS based on secure channel coding design 18.4.2 Channel-based adaptation and optimization for PLS 18.4.3 Addition of artificially interfering (noise/jamming) signals for PLS 18.4.4 Extraction of secret sequences from wireless channels 18.5 Applications of PLS in emerging technologies 18.5.1 PLS in mmWave 18.5.2 PLS in mMIMO 18.5.3 PLS in URLLC 18.5.4 PLS in IoT 18.5.5 PLS in UAV 18.5.6 PLS in CR systems 18.6 PHY-authentication against spoofing attacks 18.6.1 Channel-based PHY-authentication 18.6.2 AFE-based PHY-authentication 18.6.3 Reliability of PHY-authentication algorithms 18.6.4 Efficient and fast authentication in complex heterogeneous networks 18.6.5 Integration with the existing network infrastructure and authentication protocols 18.7 Wireless jamming attacks and countermeasures 18.7.1 Wireless jamming attacks: a brief summary 18.7.2 Wireless jamming attacks, detection, and solutions 18.8 Challenges and future research directions 18.8.1 Secrecy design based on service requirements 18.8.2 Cross-layer security design 18.8.3 PAPR of AN-based and precoding security techniques 18.8.4 Security in LOS environment 18.8.5 Robust channel estimation and channel reciprocity calibration 18.8.6 Joint design of secrecy, throughput, delay, and reliability 18.8.7 Hybrid security techniques 18.8.8 Impersonation attacks 18.8.9 Challenges related to solution against jamming attacks

543 545 545 549 551 551 552 553 553 554 559 563 565 566 567 568 568 569 570 570 572 573 574 574 576 576 576 577 580 580 580 580 580 581 581 581 582 582

Contents

xvii

18.8.10 Mixed attacks in wireless networks and cognitive security 18.8.11 A new direction for PLS 18.9 Conclusion Acknowledgment References

582 583 583 583 583

19 Physical layer security for NOMA systems Ming Zeng, Nam-Phong Nguyen, Octavia A. Dobre, and H. Vincent Poor

589

19.1 Introduction 19.2 Fundamentals of NOMA 19.2.1 Downlink NOMA 19.2.2 Uplink NOMA 19.3 Fundamentals of PLS 19.3.1 Information-theoretic secrecy 19.3.2 Metrics 19.4 PLS-enhanced NOMA 19.4.1 PLS in SISO–NOMA systems 19.4.2 PLS in MIMO–NOMA systems 19.4.3 PLS in massive MIMO–NOMA systems 19.5 Conclusion References Index

589 591 591 595 597 597 598 599 599 602 605 605 606 613

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About the editors

HüseyinArslan (IEEE Fellow) received his BS degree from the Middle East Technical University (METU), Ankara, Turkey in 1992; his MS and PhD degrees were received respectively in 1994 and 1998 from Southern Methodist University (SMU), Dallas, TX. From January 1998 to August 2002, he was with the research group of Ericsson, where he was involved with several projects related to 2G and 3G wireless communication systems. Since August 2002, he has been with the Electrical Engineering Department, at the University of South Florida, where he is a Professor. In December 2013, he joined Istanbul Medipol University to found the Engineering College, where he has worked as the Dean of the School of Engineering and Natural Sciences. In addition, he has worked as a part-time consultant for various companies and institutions including Anritsu Company, Savronik Inc., and The Scientific and Technological Research Council of Turkey. Dr. Arslan’s research interests are related to advanced signal processing techniques at the physical and medium access layers, with cross-layer design for networking adaptivity and Quality of Service (QoS) control. His current research interests are on 5G and beyond, physical layer security, interference management (avoidance, awareness, and cancellation), cognitive radio, small cells, UWB, multicarrier wireless technologies, dynamic spectrum access, co-existence issues on heterogeneous networks, and aeronautical (High Altitude Platform) communications. He has served as technical program committee chair, technical program committee member, session and symposium organizer, and workshop chair in several IEEE conferences. He is currently a member of the editorial board for the IEEE Surveys and Tutorials and the Sensors Journal. He has also served as a member of the editorial board for the IEEE Transactions on Communications, the IEEE Transactions on Cognitive Communications and Networking (TCCN), the Elsevier Physical Communication Journal, the Hindawi Journal of Electrical and Computer Engineering, and Wiley Wireless Communication and Mobile Computing Journal. Ertu˘grul Ba¸sar (IEEE Senior Member) received the BS degree (Hons.) from Istanbul University, Turkey, in 2007, and the MS and PhD degrees from Istanbul Technical University, Turkey, in 2009 and 2013, respectively. He is currently an Associate Professor with the Department of Electrical and Electronics Engineering, Koç University, Istanbul, Turkey and the Director of Communications Research and Innovation Laboratory (CoreLab). His primary research interests include MIMO systems, index modulation, intelligent surfaces, waveform design, visible light communications, and signal processing for communications. He is the author/co-author of 140 journal and conference publications and 5 patents (granted/applied).

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Flexible and cognitive radio access technologies for 5G and beyond

Recent recognition of his research includes the IEEE Communications Society Best Young Researcher Award for the Europe, Middle East, and Africa Region in 2020, Science Academy (Turkey) Young Scientists (BAGEP) Award in 2018, Turkish Academy of Sciences Outstanding Young Scientist (TUBA-GEBIP) Award in 2017, and the first-ever IEEE Turkey Research Encouragement Award in 2017. Dr. Basar currently serves as a Senior Editor of the IEEE Communications Letters and the Editor of the IEEE Transactions on Communications, Physical Communication (Elsevier), and Frontiers in Communications and Networks.

Foreword

The most critical difference of the fifth generation (5G) mobile communication technology compared to previous generations is its ability to support a wide variety of services. In other words, the 5G technology is all about flexibility and diverse applications. First generation (1G) was mainly concerned with the voice and capacity through the reuse of frequency bands (channels) utilizing analog modulation. The second-generation (2G) mobile communication was the first step to move to digital signaling and targeted to improve the quality of voice along with providing short messaging services. However, satisfying high data rate has never been a concern for both 1G and 2G networks. Nevertheless, the importance of providing a high data rate and spectral efficiency became clear with third-generation networks and with the rise of high-rate data applications such as web-browsing and multimedia communications. In parallel with the era of smart devices and high definition streaming services, the true broadband and the support for high-speed video applications were enabled with fourth-generation (4G) wireless networking. In 5G, the expectations for peak data rates will be even more; however, 5G is not all about speed and capacity. 5G is expected to support many services with very different requirements. This is like designing a vehicle that can fly in the air, drive on highways, float in the ocean, climb mountains, etc. Imagine the challenges of doing all these in the optimal manner with a single vehicle. 5G and beyond standards are expected to bring communication systems (with a single standard) with a very flexible and cognitive design to support a wide variety of services, including smart vehicles (self-driving cars), smart cities, smart homes, mobile Internet-of-everything, and remote health. 5G applications are classified under three different (but partially overlapping) groups: enhanced mobile broadband applications, massive machine-type communication applications, and ultrareliable and low-latency (uRLLC) applications. As of the start of 2020, the first commercial 5G wireless networks have been already deployed, in part or as a whole, in certain countries, thanks to the initial 5G standards completed in 2018, while the first 5G compatible mobile devices are being introduced. Beyond 5G, there may be more application classes as the number of users and the diversity in user requirements (e.g., demanded services, channel conditions, used applications, and types of mobile devices) will constantly increase over time. These different application classes have different requirements from a system design perspective. For example, latency and reliability are extremely critical for uRLLC applications. Imagine a self-driving car on a highway communicating with other cars. Any delay or error in the communication could be catastrophic, and hence it is

xxii Flexible and cognitive radio access technologies for 5G and beyond extremely important to support such applications with a design that leads to minimal delay and maximum reliability. As a result, wireless radio researchers are facing a new challenge: the design of a flexible communication system in every layer of the communication protocol stacks. Most of the current discussions on flexibility for radio access technologies (RATs) in New Radio (NR) design are conducted in a limited range by only focusing on adopting orthogonal frequency division multiplexing (OFDM)-based waveform parameters. This is not a sufficient solution not only for beyond 5G but also for the proper deployment of 5G. As of today, future 6G technologies may look like extensions of their 5G counterparts; however, new user requirements, completely new applications/use-cases, and new networking trends of 2030 and beyond may bring more challenging communication engineering problems. In the light of these, more flexible and cognitive communication paradigms are required in different layers, and particularly in the physical layer of future wireless communication systems. The envisioned new communication solutions in beyond 5G and 6G wireless networks must provide very high spectral and energy efficiencies along with ultrareliability and ultrasecurity, and more importantly, must be highly flexible to satisfy the challenging requirements of diverse users and applications. Although the intensive research efforts of the past two decades have provided us with lots of information, these have still been missing features in state-of-the-art systems and communication standards. This book is intended to be both an introductory technology survey/tutorial of important topics regarding Flexible and Cognitive Radio Access Technologies for 5G and beyond and an advanced mathematical overview intended for technical professionals in the communications industry, technical managers, and researchers in both academia and industry. We cover the enabling RATs for 5G and beyond and not only from a standard specific (like 5G) perspective but also by considering future trends beyond 5G. Rather than specific standard implementations, we provide a more generic coverage with a wide variety of technologies and their use. As a result, this book can be used as a reference book for engineers as well as a textbook/tutorial for self-teaching and advanced students. It also aims to provide researchers with new ideas and concepts related to 5G and beyond. We expect that some readers could skip the advanced mathematical treatments of the topics and still benefit greatly from this book. The presentations are both descriptive and mathematical in nature to cater to readers who need mathematical description as well as to readers who do not. This book targets individuals with a background in Electrical Engineering and basic wireless communications. This book consists of 6 parts and a total of 19 chapters dealing with both fundamental and state-of-the-art advances regarding flexible and cognitive RATs for 5G and beyond wireless networks. The first two parts are related to waveform and modulation techniques toward beyond 5G, which are two of the most important components of the physical layer design toward a new wireless communication standard. Specifically, Part I summarizes the current waveform landscape and the related issues, including 5G multi-numerology concepts. Also, in the first part, alternatives along with some projections and improvements for future waveform concepts are provided.

Foreword xxiii Part II focuses on the flexibility aspect of existing waveform and modulation formats to provide service for wide variety of applications, channel, and user conditions. The flexibility beyond what is already available for 5G is also explored. Approaches like hybrid waveforms (which are extensions of the 5G multi-numerology concept) and multidimensional modulation schemes (beyond adaptive quadrature amplitude modulation exploring index, number, shape, and power dimensions to get additional degrees of freedom) are discussed. Chapter 1 provides a generalized description and a unified perspective to the waveform concept. This generalized description includes multidimensional shaping, flexible and adaptive lattice structures, precoding and conditioning, and scalable framing. The relation of the waveform with RF front end, channel and user conditions, medium access control (MAC) layer design strategies, and with the higher layers of the protocol stacks is explored. Specifically, the importance and relation of waveform design for wide variety of applications and their requirements are discussed. Furthermore, in this chapter, 5G NR waveform structure, which has introduced the multi-numerology concept to provide service to various applications, is summarized followed by a comparison with the existing Long-Term Evolution waveform. Chapter 2 provides some background information on the OFDM waveform, its strengths, and weaknesses. OFDM by far is the most popular multicarrier waveform used in modern communication standards. This chapter particularly focuses on the main strengths and weaknesses of OFDM and discusses emerging OFDM alternatives. The performance of OFDM in various RF impairment scenarios and channel conditions are discussed and alternatives of OFDM to solve the specific issues associated with OFDM are also given in detail. In Chapter 3, a new form of interference, which arises due to the introduction of a 5G multi-numerology structure, named inter-numerology interference (INI), is investigated in detail. Models for INI, relations of INI with several parameters like numerology block size, cyclic prefix configuration, guard intervals, power, and numerology imbalance between neighboring blocks are investigated. The trade-offs between INI and other well-known interferences, i.e., inter-symbol and intercarrier interferences, are given. Solutions to minimize INI in the physical layer (e.g., flexible guard utilization) and MAC layer (like INI aware scheduling) are given. Also, extension of the multi-numerology concept to single-carrier systems is also explored. Chapter 4 further explores flexibility in waveform design by introducing the concept of hybrid waveforms and partial overlapping waveforms. Challenges and advantages of various flexible and adaptable waveform design techniques are discussed. Multi-numerology multiplexing concept is covered with flexible waveform and frame design perspectives. Furthermore, the utilization of flexible waveform design for controlling peak-to-average power ratio, out-of-band radiation, and physical layer security (PLS) is explored. The relation of the waveform design with utilized modulation formats is discussed in Chapters 5 and 6, where flexibility in modulation provides further degree of freedom that can be powerful for the design of beyond 5G RATs. In Chapter 5, the flexibility in modulation by exploring different dimensions, including time/frequency/space

xxiv

Flexible and cognitive radio access technologies for 5G and beyond

index modulation (IM), number, and shape modulations, is explored. Furthermore, a unified and inclusive framework is presented for various modulation options and the basic relation between the modulation and waveform is provided. State-of-theart frequency-domain IM approaches are further discussed in detail in Chapter 6. A special case of these approaches, which is OFDM with index modulation (OFDMIM), is revisited in this chapter by exploring the inherent flexibility features to provide adaptive and cognitive capabilities for variety of channel, interference, and RF impairment conditions. In general, this chapter tries to provide solutions for OFDM issues by introducing flexible IM features. Particularly, the flexibility aspect of OFDM-IM is explored by investigating different subcarrier mapping and subcarrier activation strategies, and effective IM solutions are discussed to ensure equal bit protection, increase robustness against asynchronous transmission and hardware impairments, and avoid deep fading/Doppler spread. Part III mainly focuses on multi-antenna transmission technologies that have been vital components of modern wireless communication standards. As a result, a contemporary waveform or a modulation option must be compatible with multipleinput–multiple-output (MIMO) systems to provide high capacity and reliability to multiple users. This part not only covers the existing MIMO systems and signaling methods but also discusses the most recent and promising multiple antenna solutions for 5G and beyond from various perspectives. Chapter 7 primarily focuses on the massive MIMO concept, which is one of the enabling technologies of 5G wireless networks. This chapter first introduces the massive MIMO technology along with an information theoretical perspective and then explores various channel models, including correlation-based and spatial-based ones. Important research topics within the framework of massive MIMO systems, including different detection algorithms, channel state information acquisition issues, are also discussed. Digital and hybrid precoding solutions are also explored for single-user and multiuser systems. In Chapter 8, beamforming and beam-management strategies are studied. Special emphasis is given on beamforming in millimeter-wave frequencies. Beam acquisition, adaptation, switching, and in general, beam-management concepts are discussed from various perspectives. Even though generalized IM and its special case of utilizing multiple antennas, which is popularly referred to as spatial modulation, is briefly covered in Chapter 5, a comprehensive coverage is also given in Chapter 9 in terms of exploiting multiple antenna systems at both transmitter and receivers. This chapter also explores various spatial modulation types, including differential, generalized, receive, link adaptive, and transmit-diversity-enhanced and precodingaided spatial modulation, and discusses the integration of other technologies, such as compressive sensing, non-orthogonal multiple access (NOMA), and millimeter-wave communications, to emerging spatial modulation systems. Exploiting and controlling the wireless propagation channel through multiple antennas can be further enhanced by integrating additional antennas or passive elements into the communication medium. Recently, techniques in this direction have gained significant interest in wireless communication community. Within this framework, Chapter 10 discusses reconfigurable intelligent surface (RIS)-assisted radio

Foreword xxv access techniques for 6G wireless networks. RIS-assisted communication is introduced as an effective beyond massive MIMO solution, to create additional and controllable propagation paths between relatively simple transmitter and receiver units. Illustrative examples are provided from basic application scenarios with multipath signaling to eliminate fading and Doppler spread effects. The most recent developments as well as potential 6G uses cases within the context of RISs are also covered in this chapter. Part IV particularly covers new frequencies and channel models for 5G and beyond standards. Channel modeling plays a vital role in the development of new standards and services and is a must for an effective physical layer design. Similarly, there is a growing recent interest in exploiting the unexplored parts of the electromagnetic spectrum to satisfy the ever-increasing demand on higher capacity, reliability, and security. Within this perspective, channel modeling issues and emerging communication paradigms such as terahertz (THz) communications and visible light communications (VLC) are covered in this part. Chapter 11 starts with the emerging channel models used in recent standards. Efficient and simple channel models are very critical for 5G and beyond, due to the need for employing multiple antennas, new frequency bands, and wide variety of applications. The classical channel models are revisited and trade-offs between different models are explained. Most recent models that are suitable for new RATs are described. Machine learning and compressive sensing solutions are highlighted for channel modeling and estimation. Use of THz frequencies for wireless communication is covered in Chapter 12. Particularly, potential application scenarios for THz communication systems are discussed, and challenges in the transceiver design, channel modeling, and physical layer design are explored. In Chapter 13, visible light spectrum is exploited to complement the future 5G and beyond networks. The applications of VLC and its integration to the existing RF technologies to provide fast, secure, and reliable communication are discussed. VLC channel models are also discussed along with proper transmission and reception techniques. Interference has always been and will always be a major problem in wireless communications due to its inherently open nature. In other words, managing interference is still one of the major challenges in the design of modern wireless communication systems, no matter how sophisticated encoding and detection techniques are used. In Part V, the interference problem in multiple dimensions is explored and radio resource allocation/coordination techniques to model, estimate, avoid, and mitigate interference are studied. Within this perspective, special emphasis is given on emerging coordinated multipoint (CoMP), NOMA, and cognitive radio solutions. In Chapter 14, the main focus is on the coordination of network and various network entities, including base stations, intelligent surfaces, and relays. This coordination can be used to control interference as well as to address different key performance metrics such as capacity, range extension, reliability, security, and latency. In addition to the well-known concepts about CoMP for 4G, this chapter also looks at the potential applications of the coordination principles for future networks. This includes coexistence issues, smart radio environments, Cloud/Fog-RAN architectures, and 6G applications. Chapter 15 emphasizes the importance of non-orthogonal

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Flexible and cognitive radio access technologies for 5G and beyond

resource sharing focusing not only on the conventional power-domain NOMA but also on grant-free accessing, waveform coexistence, and the other state-of-the-art non-orthogonal accessing schemes. In particular, this chapter explores uplink and downlink power-domain NOMA solutions along with capacity and fairness calculations. Furthermore, state-of-the-art NOMA solutions such as multicarrier low-density spreading multiple access, pattern division multiple access, and IM multiple access are investigated. In Chapter 16, spectrum sensing techniques in cognitive radio networks are explored from conventional approaches to machine learning techniques. The need for efficient utilization of frequency spectrum in 5G networks, where cognitive radio was identified as a promising technology, is discussed first in order to meet the ever-increasing demands for bandwidth in recent years. After discussing traditional spectrum sensing techniques comprising narrowband and wideband spectrum sensing, the importance of the predictive sensing technique, where machine learning techniques are employed for spectrum sensing, is highlighted. Chapter 17 expands the utilization of machine learning and artificial intelligence techniques beyond the spectrum sensing. After providing some background on machine learning and deep learning, this chapter provides how to use these tools for future wireless communications systems, including cognitive radios and networks. In the final part of this book (Part VI), we cover one of the most ignored but at the same time one of the most critical aspects of wireless communications, which is the communication security beyond the classical cryptography-based approaches in higher layers. Due to its open nature, wireless communication has always been prone to active/passive eavesdropping, spoofing, and jamming attacks, and advanced security solutions in the physical layer of the communications stack can play a very important role to further increase the overall security and reliability of the system. Chapter 18 presents the basic concepts of PLS solutions against eavesdropping, spoofing, and jamming attacks. The concepts, merits, and demerits of different popular PLS approaches with important examples and lessons are presented. Furthermore, recent applications of PLS to different emerging wireless technologies, such as millimeter-wave, massive MIMO, uRLLC communications, Internet of Things, unmanned aerial vehicles, and cognitive radio, are presented. The future research directions and recommendations along with concepts of a new dimension of PLS and cognitive security are also provided in this chapter. Finally, Chapter 19 focuses on emerging NOMA systems from a PLS perspective considering the fact that the share of the same time–frequency resources among multiple users imposes serious secrecy challenges. Single- and multi-antenna, as well as massive MIMO-assisted, NOMA systems are explored for PLS solutions, and the combination of NOMA with other advanced transmission technologies, such as simultaneous wireless information and power transfer, cooperative relaying, fullduplex communications, and beamforming, is discussed. In conclusion, this book covers not only fundamental physical layer technologies, including waveform design and OFDM, interference issues, massive MIMO and beamforming, PLS, and cognitive radio, but also many emerging technologies toward

Foreword xxvii beyond 5G and 6G such as flexible/hybrid waveforms, IM solutions, RISs, VLC and THz communication systems, and NOMA techniques. We hope that our book would be a useful resource for researchers, students, and engineers in the field of wireless communications by covering the enabling RATs not only from the perspective of existing standards and applications but also providing a unified overview of beyond 5G and even 6G technologies. Finally, we would like to thank the corresponding IET staff, particularly Ms. Val Moliere and Ms. Olivia Wilkins who helped greatly throughout the contract and publication process. We also want to thank Mr. Srinivasan N from MPS Limited and Mr. Ahmet Yazar from Istanbul Medipol University for their huge efforts with the final typesetting and corrections on this book. Prof. Hüseyin Arslan, Tampa, FL, USA Dr. Ertu˘grul Ba¸sar, Istanbul, Turkey March 2020

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List of acronyms

1G 2G 3D 3G 3GPP 4G 5G 5GC 6G A2A A2G AAC ABS ACI ACK ACO-OFDM ADC ADO-OFDM AE AES AF AFC AFE AI ALMA AM-AM AM-PM AN ANN AoA AoD AP APD APF APP AR ARQ

first generation second generation three-dimensional third generation 3rd Generation Partnership Project fourth generation fifth generation 5G core sixth generation air-to-air air-to-ground active antenna combination absolute blank subframe adjacent channel interference acknowledgment asymmetrically clipped optical OFDM analog-to-digital converter asymmetrically clipped DC biased optical OFDM auto-encoder advance encryption standard amplify-and-forward adaptive frequency correction analog front-end artificial intelligence Atacama Large Millimeter/sub-millimeter Array amplitude-to-amplitude amplitude-to-phase artificial noise artificial neural network angle-of-arrival angle-of-departure access point avalanche photodiode auxiliary particle filter application augmented reality automatic repeat request

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Flexible and cognitive radio access technologies for 5G and beyond

ASDV ASK AST AWGN B5G BA BAN BBU BD BEC BER BLER BP BPSK BS BW BWP CAP-MIMO CB-CoMP CBD CBMs CCDF CCI CCM CD CDF CDO-NOMA C-FBMC-IM CFO CI-OFDM-IM CIR CLSK CMT CN CNN CNT COMB CoMP COST CP CQI CR C-RAN CRS CS

adjacent subcarrier distance vector amplitude shift keying adaptive symbol transition additive white Gaussian noise beyond 5G bandwidth adaptation body area network baseband unit block diagonalization binary erasure channel bit error rate block error rate basis pursuit binary phase shift keying base station bandwidth bandwidth part continuous aperture phased MIMO coordinated beamforming-CoMP covariance-based detection correlation-based models complementary cumulative distribution function co-channel interference channel correlated matrix cyclostationary detection cumulative distribution function code domain O-NOMA circular convolution FBMC-IM carrier frequency offset coordinate interleaved OFDM-IM channel impulse response color shift keying cosine modulated multitone core network convolutional neural network carbon nano tube combinatorial coordinated multipoint European Cooperation in Science and Technology cyclic prefix channel quality indicator cognitive radio cloud-based radio access network CR system compressed sensing

List of acronyms CSB CS-CoMP CSI CSI-IM CSI-RS CSI-RSRP CSK CSMA/CA CST CTMA CTS CW CWT D2D DAC DAI DAPPM DAS dB dBi dBm DC DCM DCN DCO-OFDM DD DDCIR DES DF DFT DFT-s-OFDM DL DM-OFDM DNN DoA DoD DoF DPPM DPS DPS-CoMP D-rays DS DSL DSM DSSS

compressive sensing based coordinated scheduling-CoMP channel state information CSI interference measurement channel state information reference signal CSI-reference signal received power concentration shift keying carrier sense multiple access with collision avoidance carrier sensing time continuous-time moving-average clear-to-send continuous wave continues wavelet transform device-to-device digital-to-analog converter distributed artificial intelligence differential amplitude PPM distributed antenna system decibel decibel isotropic decibel over a milliwatt direct current directional channel model data center network DC biased optical OFDM direct detection double-directional CIR data encryption standard decode-and-forward discrete Fourier transform discrete Fourier transform spread OFDM deep learning dual-mode OFDM deep neural network direction of arrival direction of departure degree-of-freedom differential PPM distributed problem solving dynamic point selection-CoMP strong rays delay spread digital subscriber line differential SM direct-sequence spread spectrum

xxxi

xxxii Flexible and cognitive radio access technologies for 5G and beyond DWPT DWT E2E ED EE EESS eICIC EKF EM eMBB EMI eNB EPC EPPM ERM ESA ESIM-OFDM ESP ESPRIT EU-OFDM EVM F-AP FBB FBMC FBMC-IM FBS FCC FD FDD FDE FDM FEC FFO FFR FFT FG FIR FL FMCW FMT f-OFDM FPGA FR F-RAN F-rays

discrete wavelet packet transform discrete wavelet transform end-to-end energy detector energy efficiency Earth exploration-satellite service enhanced ICIC extended Kalman filter electromagnetic enhanced mobile broadband electromagnetic interference evolved node B evolved packet core expurgated PPM empirical risk minimization equal subcarrier activation enhanced SIM-OFDM encapsulating security payload estimation of signal parameters via rational invariance techniques enhanced unipolar OFDM error vector magnitude fog-access point filter-bank based filter bank multicarrier FBMC-index modulation flying base station Federal Communications Commission full-duplex frequency division duplexing frequency domain equalizer frequency division multiplexing forward error correction full-featured optimisation fractional frequency reuse fast Fourier transform focus group finite impulse response federated learning frequency modulated continuous wave filtered multitone filtered-OFDM field programmable gate array frequency range fog-RAN flashing rays

List of acronyms FSK FSO FSPL FSS F-UE GAN GaN GB Gbps GD Ge GF GFDM GFDM-IM GFDM-PSM GFDM-SFIM GHz gNB GNR GPS GSCM GSFIM-OFDM GSM HACO-OFDM HAP HAPS HBT HCS HEMT HetNet HFL HFSO-SCFDMA HG HII HITRAN HMM HPA HPBW HST Hz IA IBI ICI ICIC IDFT IEEE

frequency shift keying free space optical free space path loss frequency selective surface fog-UE generative adversarial network Gallium Nitride guard band gigabit per second guard duration Germanium grant-free generalized frequency division multiplexing GFDM-index modulation GFDM with pulse superposition modulation GFDM with space and frequency IM gigahertz node B graphene nano ribbon global positioning system geometry-based stochastic channel models generalized space frequency index modulation-OFDM Global System for Mobile Communications hybrid asymmetrically clipped optical OFDM high-altitude platform high-altitude platform station heterojunction bipolar transistor human-centric services high electron mobility transistor heterogeneous network horizontal federated learning Hermitian symmetry free O-SCFDMA Hermite-Gaussian high interference indicator high-resolution transmission hidden Markov model high power amplifier half-power beamwidth high-speed train hertz initial access in-band interference inter-carrier interference inter-cell interference coordination inverse discrete Fourier transform Institute of Electronics and Electrical Engineers

xxxiii

xxxiv Flexible and cognitive radio access technologies for 5G and beyond IF intermediate frequency IFFT inverse fast Fourier transform IIoT industrial Internet of Things IM-NOMA index modulation-based NOMA IM-OFDM-SS index modulated OFDM spread spectrum IMT-A International Mobile Telecommunications-Advanced InGaAs Indium Gallium Arsenide INI inter-numerology interference InP Indium Phosphide IO interacting object IoT Internet of Things IPsec Internet Protocol security IQ in-phase and quadrature IQI in-phase and quadrature imbalance IR infrared ISA inner subcarrier activation ISI inter-symbol interference ITS intelligent transportation system ITU International Telecommunication Union ITU-T Telecommunication Standardization Sector of International Telecommunication Union IUI inter-user interference JEITA Japan Electronics and Information Technology Industries Association JRC joint radar communications JT-CoMP joint transmission-CoMP KF Kalman filter KHz kilohertz KPI key performance indicator LAA lens antenna array LACO-OFDM layered ACO-OFDM LAP low-altitude platform LAS lens antenna subarray LCM Least common multiplier LDPC low-density parity-check LDS low-density spreading LDS-OFDM low-density spreading OFDM LED light-emitting diodes LEO low Earth orbit LFO latency-focused optimisation LIS large intelligent surface Li-Fi light fidelity LLR log-likelihood ratio LMS least mean square LMSE least mean squared error LO local oscillator

List of acronyms LOS LSE LSP LSTM LTE LTE-A LU LuMaMi MAC MAP MAS MB MBM MBRLLC

line-of-sight least squares estimate large-scale parameters long and short term memory Long Term Evolution LTE-Advanced licensed user Lund massive MIMO medium access control maximum a posteriori estimation multi-agent system multiband media-based modulation mobile broadband reliable low latency communication MC molecular communication MC-LDSMA multicarrier LDS multiple access MCS modulation and coding scheme MCSN multichannel sub-Nyquist METIS Mobile and wireless communications Enablers for the Twenty-twenty Information Society MF matched filter MHz megahertz MILTAL Millimeter Wave and Terahertz Technologies Research Laboratories MIMO multiple-input multiple-output MIS medium intelligent surface MISO multiple-input single-output MiWEBA Millimeter-Wave Evolution for Backhaul and Access ML machine learning MLE maximum likelihood estimation MLP multi-layer perceptron MLSTM multivariate long and short term memory mMIMO massive multiple-input multiple-output mmMAGIC Millimeter-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications MM-OFDM multiple-mode OFDM MMSE minimum mean square error mMTC massive machine type communications mmWave millimeter-wave MNO mobile network operator MPA message-passing algorithm MPC multi-path components MPPM multi-pulse PPM MRC maximum ratio combining MRI magnetic resonance imaging

xxxv

xxxvi Flexible and cognitive radio access technologies for 5G and beyond MRS MRT MS MSE MSE-OFDM MUD mURLLC MUSIC NACK NB-DI NET NFV NGSM NGWN NLOS NMSE NOMA NPI NR NRZ NSA NSN O2I OAI OB-MMSE O-CDMA OFDM OFDMA OFDM-AIM-ACM

multi-robot system maximum ratio transmission mobile station mean squared error multi-symbol encapsulated OFDM multi-user detection massive URLLC MUltiple SIgnal Classification negative acknowledgment narrowband dominant interference network network function virtualization non-geometric stochastic models next generation wireless network non-line-of-sight normalized mean squared error non-orthogonal multiple access noise plus-interference New Radio non-return-to-zero non-standalone narrow subcarrier numerology outdoor-to-indoor open air interface ordered block minimum mean-squared-error optical code division multiple access orthogonal frequency division multiplexing orthogonal frequency division multiple access OFDM with joint adaptive index modulation and adaptive constellation modulation OFDM-AIM-FCM OFDM with adaptive index modulation and fixed constellation modulation OFDM-DM OFDM-differential modulation OFDM-FSK OFDM with filter shift keying OFDM-GIM generalized OFDM with index modulation OFDM-IM OFDM with index modulation OFDM-ISIM OFDM with interleaved subcarrier index modulation OFDM-PSM OFDM with pulse superposition modulation OFDM-SIS OFDM with subcarrier index selection OFDM-SNM OFDM with subcarrier number modulation OFDM-SPM OFDM with subcarrier power modulation OFDM-STSK OFDM-aided space-time shift keying OFDM-VIM-VCM OFDM with variable index modulation and variable constellation modulation

List of acronyms OI OLOS OMA OML OMP O-NOMA OOB OOBE OOK OPPM OQAM O-SCFDMA OSI OSTBC OTDM OTFS OWC PA PAM PAPR PBCH PC PCB PD PDF PDMA PD-NOMA PDO-NOMA PDP PE PELs PER PGP PHY PL PLC PLS PMF PMI PM-MIMO PMMs PMs PN PNA

overload indicator optical line-of-sight orthogonal multiple access Oleson Microwave Labs orthogonal matching pursuit optical non-orthogonal multiple access out-of-band out-of-band emission on-off keying overlapping PPM offset quadrature amplitude modulation optical single carrier frequency division multiple access open systems interconnection orthogonal space time block codes orthogonal transform division multiplexing orthogonal time frequency and space optical wireless communication power amplifier pulse amplitude modulation peak-to-average power ratio physical broadcast channel phosphorous coated printed circuit board power-domain probability density functions pattern division multiple access power-domain NOMA power domain O-NOMA power delay profile power efficiency penetration losses packet error rate pretty good privacy physical layer path loss power line communication physical layer security probability mass function precoding matrix indicator post-massive MIMO propagation-motivated models physical channels models phase noise performance network analyzer

xxxvii

xxxviii Flexible and cognitive radio access technologies for 5G and beyond POI PPM PS PSD PSK PSM PSS PTS PU PUEA PWM QAM QCL Q-D QoE QoS QPSK QSM QuaDRiGa RA RACH RAN RAT RB RC RE REM RF RI RIS RL RMS RNN RNTP ROC R-rays RRC RRH RS RSM RSRP RSRQ RSS RSSI RT

points of interest pulse position modulation phase shifter power spectral density phase-shift keying precoding aided SM primary synchronization signal partial transmit sequences primary user primary user emulation attack pulse width modulation quadrature amplitude modulation quantum cascade laser quasi deterministic quality-of-experience quality-of-service quadrature phase shift keying quadrature SM QUAsi Deterministic Radio channel Generator reconfigurable antenna random access channel radio access network radio access technology resource block raised cosine resource element radio environment map radio frequency rank indicator reconfigurable intelligent surface reinforcement learning root mean square recurrent neural network relative narrowband transmitted power region of convergence random rays root RC remote radio head reference signal receive SM reference signal received power reference signal received quality received signal strength received signal strength indicator ray-tracing

List of acronyms RTS RTT RVQ Rx S/MIME SA SAE SAP SC SCM SCMA SCS SDMA SDN SDR SE SEP SFR S-FTP SGD S-HTTP SIC SIMO SIM-OFDM SINR SIR SIS SISO Si SiGe SM SM F-OFDM SMa SM-GFDM SM-OFDM SMSS SMT SNR SOP SPAM s-parameter SPNIR SPP SRM SS

request-to-send round trip time random vector quantization receiver secure/multipurpose internet mail extensions standalone stacking auto-encoders subcarrier activation pattern single-carrier spatial channel model sparse code multiple access subcarrier spacing space division multiple access software-defined networking software-defined radio spectral efficiency symbol error probability soft frequency reuse secure file transfer protocol stochastic gradient decent Secure HyperText Transfer Protocol successive interference cancellation single-input multiple-output subcarrier index modulation OFDM signal-to-interference-plus-noise ratio signal-to-interference ratio small intelligent surface single-input single-output Silicon Silicon Germanium spatial modulation spatial modulation F-OFDM suburban macro spatial modulation-GFDM spatial modulation-OFDM swept-MB spectrum sensing staggered modulated multitone signal-to-noise ratio secrecy outage probability superposed PAM scattering parameters signal-to-phase-noise-interference-ratio surface plasmon polariton structural risk minimization synchronization signal

xxxix

xl Flexible and cognitive radio access technologies for 5G and beyond SSDF SSH SSK SSL SSP STA STBC STO STSK SU sub-THz SURLLC SV SVD SVM SWIPT TAS TBS TCM TCP TD TDD TDL TDMA THz TKIP TLS TO ToA TP TR TRN TRxP TS TS-OOK TSP TTI TUBITAK TV Tx UAV UDN UE UFMC

spectrum sensing data falsification Secure SHell space-shift keying solid state lighting small-scale parameters station space-time block code sample timing offset space-time shift keying secondary user sub-terahertz secure URLLC Saleh-Valenzuela singular value decomposition support vector machine simultaneous wireless information and power transfer transmit antenna selection terrestrial base station trellis coded modulation transmission control protocol time-domain time division duplex tapped delay line time division multiple access terahertz temporal key integrity protocol transport layer security time offset time-of-arrival transmission point Technical Report transport transmission reception point Technical Specification time spread OOK time-spatial propagation transmission time interval Scientific and Technological Research Council of Turkey television transmitter unmanned aerial vehicle ultra-dense network user equipment universal filtered multicarrier

List of acronyms UF-OFDM-IM ULA UMa UMi umMIMO U-OFDM UPA URLLC USB UV UV-LIDAR UW V2I V2V V2X V-BLAST VCR VFL VGA VL VLC VLCC VNA VPPM VR WDMA WEP WF Wi-Fi WiGig WiMax WLAN W-OFDM WPAN WSN WTB ZF ZP ZT

universal filtered OFDM with index modulation uniform linear array urban macro urban micro ultra-massive MIMO unipolar OFDM uniform planar array ultra-reliable low latency communication universal serial bus ultraviolet ultraviolet light detection and ranging system unique word vehicle-to-infrastructure vehicle-to-vehicle vehicle-to-everything Vertical Bell Labs Layered Space-Time virtual channel representation vertical federated learning variable gain amplifier visible light visible light communication Visible Light Communication Consortium vector network analyzer variable PPM virtual reality wavelength division multiple access wired equivalent privacy Wiener filter wireless fidelity wireless gigabit Worldwide Interoperability for Microwave Access wireless LAN windowed-OFDM wireless personal area network wide subcarrier numerology wavelength transform based zero-forcing zero-padding zero-tail

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Part I

Waveform design: an overview

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Chapter 1

Introduction to waveform design Ahmet Yazar1 and Hüseyin Arslan1,2

In this chapter, fundamental concepts are introduced for the waveform design. Generalized definition of the waveform is given to provide a basis for the discussions in the first two parts. Several relationships for the waveform design are investigated, and then application requirements of different wireless communications standards are discussed. Impacts of the waveform design on radio access technologies (RATs) are examined. At the end, standardization perspective of the waveform design is presented through the example of fifth generation (5G) new radio (NR) frame.

1.1 Introduction In the old wireless communication standards, we have only a limited number of applications and services due to the technological limits and old trends during those years. Nevertheless, the current and new generations of communication systems are being developed by considering diverse applications and use cases [1]. Application richness and the level of technological development have increased extraordinarily in the last 10 years in the field of modern wireless communications. As shown in Figure 1.1, we are witnessing a wide variety of applications nowadays, including broadband and media everywhere, smart vehicles and transport, critical services and infrastructure control, critical control of remote devices, human–machine interaction and sensor networks. There is a need for a flexible communications infrastructure that can meet different application requirements in the current and future wireless systems [2–4]. Indeed, new generation communications standards are constituted based on a wide variety of wireless communications applications, user conditions and new use cases that have emerged in the recent years [5–7]. Different layers of the communications stack, such as physical, medium access control (MAC) and network layers, can play an important role in using several core technologies while meeting possible requirements of the future wireless systems. It is worth noting that it is not effective to meet all requirements with a single layer, subsystem or technology. Within this context, there should be a work distribution between communications layers, the related subsystems and available technologies.

1 2

Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Turkey Department of Electrical Engineering, University of South Florida, Tampa, USA

4 Flexible and cognitive radio access technologies for 5G and beyond

Figure 1.1 Application richness and diversity for new generation communications systems Waveform is one of the core components of the physical layer (PHY) design. Generally, the waveform is designed considering the whole communications system. It is like the heart of the communications. The other components are designed considering the chosen waveform in communications standards. The waveform design is strongly related to application, channel, user conditions, radio frequency (RF) front end, MAC design and the higher layers of the communication stack. This chapter aims to describe the role of the waveform design in wireless communications. In Section 1.2, the generalized definition of the waveform is given in detail. Relationships of channel and RF impairments with the waveform design are investigated in Section 1.3. Application requirements of different standards are discussed in Section 1.4. Impacts of the waveform design on different RATs are examined in Section 1.5. After that, standardization perspective of the waveform design is presented in Section 1.6. Finally, our conclusions are provided in Section 1.7.

1.2 The generalized definition of a waveform The main components of a waveform are defined in this section to understand the concept of the waveform design in a better way. Basically, waveform is a physical

Introduction to waveform design

5

signal that contains information. Data bits are mapped to the physical signal through a proper waveform. Also, additional symbols (e.g. redundancy, preconditioning like precoding and guard utilization, noise) are the parts of the physical signal. These signals occupy physical resources (like bandwidth (BW), time, space, code, power) in multidimensional hyperspace. Figure 1.2 shows the main components of a waveform design, including lattice structure, pulse shape and frame structure. Lattice structure is a multidimensional resource mapping and each point shows a location of one resource element [8]. The resource element is the smallest discrete part that contains physical signal on a lattice structure. For example, the resource element is one symbol and one subcarrier for 5G NR in time–frequency planes, respectively. The possible spacings between lattice points give numerology structures for a waveform. Lattice structure can be uniform or non-uniform. If the lattice structure is uniform, then the lattice spacings are fixed and there is a single-numerology waveform. For example, the spacings between Long Term Evolution (LTE) lattice points are fixed and LTE employs a single-numerology waveform. However, 5G NR uses a flexible non-uniform lattice structure that indicates multiple numerologies. Additionally, both LTE and 5G NR use a multidimensional lattice on time–frequency planes. More dimensions are possible in the future generations of communications systems. Pulse shape (also known as filter) gives the main characteristic to a waveform by deciding how to transmit the symbols on lattice points [8]. As a result, waveform defines the physical shapes that contain energy in the hyperspace. The variances of energy in the hyperspace give the localization of a pulse shape. Moreover, correlation between the lattice points is determined by the pulse shapes [8]. This correlation and the localization show the orthogonality of a waveform design. In the literature, the following filters are utilized while designing waveforms [8]: rectangular, hanning (raised-cosine), exact hamming, exact Blackman, tapered-cosine-in-time, taperedcosine-in-frequency, root-raised-cosine, Mirabbasi–Martin, prolate, optimal finite duration pulses, Kaiser, modified Kaiser, Gaussian, IOTA, Hermite and extended Gaussian. These pulse shapes have different localization characteristics.

Waveform

Frame (MAC layer)

Pulse shape

Numerology assignment

Lattice structure

Waveform processing

Fixed Pulse shape processing Multiple domains

Spacings between points (numerology)

Duplexing Flexible

Fixed (e.g. LTE)

Multiple access

Flexible (e.g. 5G NR)

Guard utilization

OMA

NOMA

Figure 1.2 The main components of the waveform design

6 Flexible and cognitive radio access technologies for 5G and beyond Frame structure can be defined as a packaging (formation) of multiple-user information because waveform is the process of generating the collective physical signal corresponding to multiple users (and/or multiple information data) that occupy the hyperspace. Waveform characterizes the multiple-access scheme using the frame structure. The multiple-access scheme controls the sharing of resources by multiple users and multiple shapes in the hyperspace. Individual pulse shapes are combined under a frame structure to form a waveform. The frame controls the interaction between the pulse shapes by utilizing waveform processing. For example, lattice points can be used as guard intervals if it is beneficial for the overall performance of a waveform design. The lattice points do not need to carry data, and they are controlled by the frame structure. The waveform frame is constituted by the scheduling units in a communications system. Within this context, numerology assignment, waveform processing (pulse shape processing and guard utilization), orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) and duplexing decisions are applied via the frame structure of a waveform design. The details of the OMA and NOMA concepts are discussed in Chapter 15.

1.3 Relationships of channel and RF impairments with a waveform Example interactions between several requirements, communications layers, channel structure and core technologies are shown in Figure 1.3. They are all connected to each other strongly with important relationships. If the waveform can be related with the other parts of communications, then the overall system can be designed properly. For the relationships, effects of wireless channel and RF hardware impairments are discussed in this section. Application requirements and impacts of the waveform design on RATs are investigated in the following sections. There are several relationships of MAC and network layers with the waveform design as shown in Figure 1.3. A MAC layer decides the distribution of resources among the users. It controls the resource allocation and scheduling mechanisms together with the waveform frames [9]. As an example relation for the network layer, if there are overlappings in any lattice domain of a waveform design, synchronization can be difficult in that domain. Overlapping can cause an interference if there is a misalignment in anyway. Therefore, synchronous networks have problems with the waveform designs that have overlapping in any lattice domain. The waveform design has strong relations with the wireless channel. First, singlecarrier and multi-carrier waveform designs have different impacts on the channel. Single-carrier waveforms use whole BW for one carrier. If transmission BW is larger than coherence BW, the channel becomes frequency-selective. In multi-carrier waveforms, transmission BWs are divided into subcarriers. If narrow bands are less than coherence BW, a flat response is received from each portion of the BW. Additionally, the lower limit of subcarrier spacing is generally determined with the coherence time to handle inter-carrier interference (ICI) [10]. Single-carrier waveforms are better with respect to Doppler spread. Hence, it is better to prefer single-carrier waveforms

Introduction to waveform design

Application requirements Environmental effects Hardware impairments

7

Waveform design

Channel structure

PHY design

System requirements and challenges MAC design

Resource constraints

Network operator requirements

Application perspective User and channel perspective Network operator perspective System design perspective Technology perspective

Core technologies

Network design

Mutual interaction Unilateral interaction

Figure 1.3 Relationships between the requirements and several communications layers

for a time-selective channel (dispersive in frequency domain). On the other hand, multi-carrier waveforms can be used for frequency-selective channels (dispersive in time domain). For example, if there is a selectivity in frequency, localized pulses in frequency domain need to be used for the waveform design. If there is a selectivity in time, then localized pulses in time domain need to be used. As a last example, if there is a selectivity in space, the waveform design needs localized beams in an angular domain. Depending on the channel requirements, a proper trade-off has to be done to design a suitable waveform. Understanding the relation between RF impairments and waveform is critical for the design of proper waveforms. Within this context, single-carrier and multicarrier waveform designs have different significant effects on the RF impairments. For multi-carrier waveforms, the increasing number of subcarriers results in more power amplifier (PA) non-linearities. As PAs are not linear in nature, they show nonlinearities after specific thresholds for the input powers. PA non-linearities cause in-band interference and interference with neighbouring band that constitutes out-ofband emission (OOBE). For single-carrier waveforms, PA non-linearities increase as roll-off factor decreases or modulation order increases. As other RF impairment, phase noise affects the single-carrier waveforms with time varying and correlated noise. Besides, phase noise causes ICI for the multi-carrier waveforms. It is an important problem especially in high frequencies. Moreover,

8 Flexible and cognitive radio access technologies for 5G and beyond the effect of sample timing offset (STO) is different on single-carrier waveforms and multi-carrier waveforms. Loss of the optimal sampling phase is the problem caused by STO in single-carrier waveforms. For multi-carrier waveforms, STO causes ICI. In [11], different waveforms are compared from the carrier frequency offset together with the in-phase and quadrature (IQ) imbalance perspectives. Non-identical amplitudes and not fixed 90 degree phase difference between I - and Q-branch lead to IQ imbalance. Ideally, I and Q should be orthogonal. Single-carrier waveforms are generally more robust against the IQ imbalance problems because of the ability of better spectrum controlling. In Chapter 2, the RF impairments are investigated for orthogonal frequency division multiplexing (OFDM) waveform. Frequency offset, symbol timing error, sampling clock offset, phase noise, PA non-linearities and IQ impairments are discussed in detail.

1.4 Application requirements of cellular use cases and wireless fidelity (Wi-Fi) standards A detailed analysis is presented for the requirements of use cases and standards for wireless communications in this section. Cellular and Wi-Fi, namely IEEE 802.11 family of standards, communications are discussed. Applications are mapped to the requirements for the standards of cellular and Wi-Fi communications. In LTE, i.e., fourth generation, and other previous generations, different requirements were not grouped and there was only a single service type in a single standard for cellular communications. However, different application requirements are grouped under various service types in one standard starting from 5G NR. Thus, the requirements of cellular communications are investigated considering three main service types of 5G NR: enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC) and massive machine type communications (mMTC). For Wi-Fi communications, different application requirements are grouped under several standards rather than the service types in one standard. In this way, requirements are analysed under several Wi-Fi standards that include IEEE 802.11ay, 802.11ad, 802.11be, 802.11ax, 802.11ac and 802.11ah. In light of these standards, one can state that a set of new requirements generally brings a new Wi-Fi standard. It can be expected to see more sophisticated service types for beyond 5G and a higher number of standards for Wi-Fi because diversity requirement continues to increase. Additionally, different standards might have a similar set of system requirements. As an example, mMTC service type in 5G has almost the same set of requirements with IEEE 802.11ah standard.

1.4.1 Cellular communications use cases As mentioned, applications are grouped into three service types in 3rd Generation Partnership Project (3GPP) Release 15 regarding their requirements. Figure 1.4 shows

Introduction to waveform design High data rate up to 20 Gbps

eMBB

mMTC Low complexity 10s of bits/s High capacity 100×

High throughput 10×

High connectivity 1,000,000 nodes/m2

Long battery life +10 years

High reliability 99.999%

Low cost

Low latency ro ) =

f (r)dr = ero /σr = ePAPRo . 2

2

(2.7)

ro

Assuming that all N samples are independent in an OFDM symbol, the complementary cumulative distribution function (CCDF) of the PAPR can be calculated as follows: CCDF[PAPR] = 1 − (1 − ero /σr )N = 1 − (1 − ePAPRo )N . 2

2

(2.8)

Figure 2.5 shows the CCDF of the PAPR for various N . The PAPR increases with an increase in N . It should be noted that the OFDM signal is oversampled before passing through the digital-to-analog converter (DAC) in practical communication systems and the analog signal after the DAC usually has a higher PAPR value compared to the given PAPR expression in (2.8).

2.2.2 Performance in multipath channel A transmitted signal may arrive at a receiver either directly (i.e., line-of-sight (LOS)) or after being reflected from various objects in the environment (i.e., non-line-of-sight (NLOS)). These reflected signals from different surfaces travel through different paths and accordingly reach the transmitter with different delays and gains. This propagation environment is usually referred to as a multipath channel and illustrated in Figure 2.6.

36

Flexible and cognitive radio access technologies for 5G and beyond

NLOS

LOS

Figure 2.6 Wireless communications in the presence of a multipath propagation Multipath propagation creates small-scale (also known as Rayleigh) fading effects on the received signal, as shown in Figure 2.7. The performance of OFDM systems in time- and frequency-dispersive multipath channels is described in this section.

2.2.2.1 Time-dispersive multipath channel The multipath channel causes dispersion in the time domain and produces ISI. The dispersion in the time domain might lead to a frequency-selective fading, depending on the transmission bandwidth of the signal. The coherence bandwidth of the channel (Bc ) is defined as the bandwidth, in which the channel frequency response can be considered as flat (i.e., highly correlated). It is inversely proportional to the delay spread in the propagation environment. When the transmission bandwidth exceeds the coherence bandwidth of the channel, the signal experiences a frequency-selective fading. The frequency-selective fading and ISI result in significant communication performance degradation. Channel equalizers are used to compensate for the ISI effect of the multipath channel. The complexity of these equalizers depends on the number of resolvable channel taps. Single-carrier systems transceive signals with shorter symbol duration compared to multicarrier systems, which utilize the same transmission bandwidth, and they resolve more channel taps. As a result, sophisticated equalizers are required for broadband single-carrier systems. OFDM has been promoted for broadband communications due to its high performance in time-dispersive channels. The bandwidth of each subcarrier is set to be less than the coherence bandwidth of the channel. Hence, each subcarrier experiences a single-tap flat fading channel, and no complex multi-tap channel equalizer is needed. To avoid the multipath components that leak from the tail of the previous symbol to the head of the following symbol, OFDM symbol duration is extended by adding a guard interval with a period of Tg to the beginning of each symbol either with zero-padding (ZP) or CP. The guard interval should be longer than the maximum

OFDM and alternative waveforms

Received signal power (dB)

Path loss

37

Shadow fading Rayleigh fading

Log (distance) Rayleigh fading

Aτ (∆τ )

H( f )

1 Bc ≈ 5τRMS

Time dispersion

τ

RMS

f

∆τ

pt (∆f )

h(t)

Frequency dispersion

Tc ≈

fc

fD

fc fc + fD

∆f

1 D

t

Figure 2.7 Time- and frequency-dispersive multipath channel

excess delay of the channel, which is defined as the delay between the first and last received paths over the channel. The utilization of CP is more common than ZP due to its advantages against various impairments, as discussed in the following section. It should be noted that the CP constitutes redundant information, and hence, it reduces the spectral efficiency. Also, a portion of the transmission power is wasted for CP, and it reduces the power efficiency as well. The CP duration is hard-coded in 4G LTE and does not take into account the individual user’s channel delay spread. As a result, the fixed guard interval leads to a degradation in the spectral and power efficiencies. The channel can be considered as a filter, and a transmitted signal arrives at a receiver after convolving with the channel. This convolution operation in the time domain corresponds to a multiplication operation in the frequency domain if the channel is circular. The CP part of the OFDM signal ensures the circularity of the channel and enables easy FDE with a simple multiplication operation. Assuming the channel is slowly varying in time, the received signal at each carrier can be expressed as follows: Ym (k) = Xm (k)H (k),

(2.9)

38

Flexible and cognitive radio access technologies for 5G and beyond

Freq. TX OFDM signal

Channel frequency response

Freq.

Freq. RX OFDM signal

Figure 2.8 Effect of time-dispersive (frequency-selective) channel on OFDM subcarriers

where H (k) represents the complex channel frequency response, which is assumed to be constant within a subframe. It should be noted that the channel frequency response is subcarrier dependent, as shown in Figure 2.8. However, the variation across the subcarriers is smooth. In other words, the channel frequency response of closely spaced subcarriers is correlated. Once the channel frequency response is estimated, the equalization can be performed as shown in the following equation: Yequalized (k) =

Xm (k)H (k)H ∗ (k) . |H (k)|2

(2.10)

The BER performance of OFDM in time-dispersive/frequency-selective multipath fading channel could be poor due to the deep fading in some carriers. In single-carrier systems with perfect channel equalization capability, multipath time dispersion allows path diversity, and in theory, it provides a BER performance close to the AWGN channel. However, in OFDM, the path diversity is lost as OFDM converts the frequency-selective channel to frequency flat channel for each subcarrier. Each subcarrier experiences a random fading with an envelope that has a Rayleigh distribution. The error floor and poor performance in fading channels can be handled with the forward error correction and frequency interleaving mechanisms. Figure 2.9 presents the BER performance of OFDM systems in bothAWGN and multipath fading channels.

OFDM and alternative waveforms

39

100 Time-dispersive multipath channel AWGN channel

10–1

BER

10–2

10–3

10–4

10–5

0

5

10

15

20

25

30

SNR (dB)

Figure 2.9 BER performance of OFDM in time-dispersive multipath channel and AWGN channel

2.2.2.2 Frequency-dispersive multipath channel Mobility in free space or LOS multipath propagation environments, where a single dominant multipath component exists, leads to a Doppler shift issue. Handling the Doppler shift is straightforward, and pilot-based techniques can be used to estimate and compensate the frequency offset resulting from the Doppler shift effect. However, if the number of multipath components is large, and they arrive at a receiver from different angles, Doppler spread occurs. Doppler spread is a combination of different Doppler shifts, and unlike the Doppler shift issue, it is hard to deal with due to its random nature. Mobility in a multipath channel causes dispersion in the frequency domain and produces inter-carrier interference (ICI) for OFDM systems. The dispersion in the frequency domain might lead to a time-selective fading, depending on the symbol duration of the signal. The coherence time of the channel (Tc ) is defined as the duration, in which the channel time response can be considered as flat (i.e., highly correlated). It is inversely proportional to the Doppler spread in the propagation environment. When the symbol duration exceeds the coherence time of the channel, the signal experiences a time-selective fading. The maximum Doppler shift occurs when a multipath component and the direction of mobility are aligned (i.e., the angle of arrival is 0◦ or 180◦ with respect to the direction of travel). The maximum Doppler shift can be calculated as follows: fd max =

vfc v = , λ c

(2.11)

40

Flexible and cognitive radio access technologies for 5G and beyond –5

–10

ICI power (dB)

–15

–20 Universal upper bound Theory

–25

–30

–35 0

0.05

0.1

0.15

0.2

0.25

0.3

Normalized Doppler

Figure 2.10 ICI effect of frequency-dispersive multipath channel on OFDM

where v is the speed of the mobile, fc is the carrier frequency, and c is the speed of the light. The ICI created by Doppler spread can degrade the performance of OFDM systems seriously. A theoretical ICI power derivation due to Doppler spread issue [22] and a universal upper bound [23] are given, respectively, in the following equations: PICI =

N ( fd max Ts )2 1 , 2 (k − i)2 k=1

(2.12)

2π fd max Ts . 12

(2.13)

k=i

PICI ≤

Figure 2.10 shows the effect of Doppler spread on ICI power as a function of normalized Doppler spread with respect to the subcarrier spacing. As the mobility (e.g., vehicular speed) increases, the maximum Doppler shift and spectral spreading increase as well. Accordingly, it leads to more ICI and degrades the communication system performance.

2.2.3 Performance with impairments Several impairments degrade the performance of the OFDM systems if the system is not properly designed. The integrated design and research for the baseband and RF challenges of OFDM systems require a thorough understanding of these impairments. This section presents critical impairments and their effects on OFDM systems.

OFDM and alternative waveforms

41

2.2.3.1 Frequency offset The frequency offset occurs when there is a difference between the transmitter local oscillator (LO) and receiver local oscillator. It results in ICI and destroys the orthogonality of subcarriers. The frequency offset is usually compensated by using adaptive frequency correction; however, any residual error results in degraded system performance. Assume a time-domain OFDM signal that encounters a frequency offset issue as given in the following: IFFT

X (k) → x(n)

Frequency Offset

→

FFT

y(n) → Y (k),

(2.14)

where y(n) represents the received baseband signal with frequency offset error, and Y (k) denotes the recovered data symbols. After going through the detailed derivation, the recovered symbols can be related to the transmitted symbols considering a frequency offset of δf as follows: Ym (k) ≈ Xm (k)Sm (k, k) +

N −1

Xm (l)Sm (l, k),

(2.15)

sin(π(l − k + ε)) jπ (l−k+ε) j2π ε(m−1)(Ns /N ) j2π ε(Ns −N /N ) e e , e π(l − k + ε)

(2.16)

l=0,l=k

where Sm (l, k) = ε=

δf . f

(2.17)

The first term in (2.15) is equal to the transmitted symbol multiplied with an attenuation and phase rotation term that depends on ε and OFDM symbol index (please note that it does not depend on carrier index k, and hence the effect of frequency offset on the subcarriers is the same for all carriers). Therefore, the term S(k, k) introduces a constant phase shift of 2πεmNs /N and an attenuation of sin (π ε)/π ε in magnitude. In addition to the attenuation of desired symbols, there is also interference between subcarriers. The second term in (2.15) represents the interference from other subcarriers, which is often referred to as ICI. Figure 2.11 shows the effect of frequency offset on the constellation of received symbols. It should be noted that the frequency offset error introduces both phase rotation and ICI. The phase rotation can be handled by simple pilot based phase tracking techniques. However, ICI creates a circular noise-like effect on the constellation and needs and requires more advanced receiver algorithms. The ICI power due to the frequency offset can be theoretically calculated using the following equation [4]: ⎧ 2 ⎫ ⎬

N −1 ⎨ π 2 fo 2 PICI = E Xm (l)Sm (l, k) = , (2.18) ⎩ 3 f ⎭ l=0,l=k

42

Flexible and cognitive radio access technologies for 5G and beyond

1.5

W/o Frequency Offset With Frequency Offset

1

Quadrature

0.5

0

-0.5

-1

-1.5 -1.5

-1

-0.5

0

0.5

1

1.5

In-Phase

Figure 2.11 Effect of frequency offset on the constellation of the received symbols 5 0

ICI power (dB)

–5 –10 –15 –20 –25 –30 –35 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Normalized frequency offset

Figure 2.12 ICI effect of frequency offset on OFDM where fo is the fractional frequency offset and f is the subcarrier spacing. Figure 2.12 illustrates the ICI power due to the frequency offset using (2.18). Also, the ICI effect from an active subcarrier to the neighboring subcarriers is formulated as follows:

fo 2 PICI (k) = sinc k − . (2.19) f

OFDM and alternative waveforms

43

0 Normalized offset=0.01 Normalized offset=0.05 Normalized offset=0.20

-10

ICI power (dB)

-20 -30 -40 -50 -60 -70 -80 0

50

100

150

200

250

Subcarrier index

Figure 2.13 ICI effect of frequency offset from an active subcarrier to the neighboring subcarriers

Figure 2.13 shows the impact of an active subcarrier on other subcarriers as a function of frequency offset and spacing between the active subcarrier and the subcarriers that it interferes with. As the frequency offset increases, the ICI power that a subcarrier creates on the neighboring subcarriers increases as well. When the frequency offset amount is large, the impact is not only ICI but also the shift of subcarriers, which leads to the demodulated data at the receiver being in a wrong subcarrier position with respect to the subcarrier mapping deployed at the transmitter. For large values of frequency offset, the offset can be split into integer and fractional parts ftotal = fint + fo = f + εf , where is an integer and −0.5 ≤ ε ≤ 0.5. The fractional part causes ICI, as discussed above in detail. The integer part corresponds to the multiples of the subcarrier spacing f , which can cause subcarrier shift to the left or right depending on the sign of the offset. In other words, the symbol that is transmitted on the kth subcarrier appears on the neighboring carriers. Although the integer part of the frequency offset creates a significant problem, estimating it is quite easy, especially when pilots are transmitted along with data. The offset can be estimated by correlating the known pilots with the received signal, and the frequency offset is compensated accordingly.

2.2.3.2 Symbol timing error In OFDM systems, the symbol timing synchronization errors at the receiver causes two major problems depending on whether the timing error is toward the left of the perfect synchronization point or the right.

44

Flexible and cognitive radio access technologies for 5G and beyond

When the timing synchronization error is to the left of the perfect synchronization position, the timing error only leads to a rotation of symbols for small offset values. However, if the timing offset is large, it causes ISI as well. Usually, the CP duration Tg is selected larger than the maximum excess delay of the channel τmax . Therefore, we have an error margin of Tg − τmax . If the timing position is shifted to a point within this margin of the CP duration, then, only carrier-dependent phase rotation is observed. However, if the shift is larger than this margin, then ISI occurs as well. Rotation of symbols can be folded into the estimated channel and corrected easily if the timing offset is smaller than the unused part of the CP. The unused part of the CP is the part, which does not interfere with the previous symbol. Assume a time-domain OFDM signal that encounters time offset issue as given below: IFFT

X (k) → x(n)

Symbol timing error

→

FFT

y(n) → Y (k),

(2.20)

where transmitted symbols are represented by X (k) and the baseband equivalent of the time-domain signal is represented by x(n). The symbol timing error is a result of an incorrect start position assumption of the OFDM symbol. Therefore, y(n) is nothing but the shifted version of x(n) in the time domain. For example, if there is a timing error of θ , y(n) can be expressed as follows: y(n) = x(n ± θ) =

N −1

X (k)e j(2π k/N )(n±θ) .

(2.21) (2.22)

k=0

The sign of θ depends on whether the sampling is done before the exact start position or after the exact position. In the following step, Y (k) can be calculated from y(n) using FFT as follows: N −1 N −1 1 j(2π l/N )(n±θ) e−j(2π kn/N ) X (l)e Y (k) = N n=0 l=0 = X (k)e± j(2π kθ /N ) .

(2.23)

Equation (2.23) shows that the timing offset of θ causes only rotation on the recovered data symbols. Also, the value of the recovered symbol depends only on the transmitted data, but not on the neighboring carriers. In other words, the symbol timing error does not destroy the orthogonality of the subcarriers, and the effect of timing error is a phase rotation that linearly changes with the carriers’ order. When the timing synchronization error is to the right of the perfect synchronization position, the timing offset causes ISI, ICI, and phase rotation. This type of timing error is undesirable since it causes a loss of subcarrier orthogonality and leakage to the next OFDM symbol, which leads to ICI and ISI, respectively. As a result, the symbol timing is often intentionally slightly shifted toward the CP (i.e., toward

OFDM and alternative waveforms

45

the left of the actual estimated timing position) so that any possible error in symbol timing estimation that might create the loss of orthogonality can be avoided. Even though this intentional bias in synchronization prevents the loss of orthogonality of the subcarriers and ICI, it results in the effective channel frequency response to be less correlated due to the additional carrier-dependent phase shift. As a result, the channel estimation performance degrades since the noise averaging effect is reduced. However, a well-designed channel estimation algorithm can take care of this problem. The effects of symbol timing offset on the OFDM systems are shown in Figure 2.14. As mentioned earlier, the use of CP in OFDM avoids the ISI. However, there is another advantage of the use of CP. As discussed above, when there is a timing offset in the synchronization, and if the timing offset falls within the ISI-free part of the CP interval, the system still maintains orthogonality. If ZP is used instead of CP, the system has immunity against ISI, but it cannot maintain the orthogonality if there is any timing offset in the synchronization. Therefore, the use of CP is preferred in OFDM systems compared to the use of ZP in general.

(a)

(b)

1

1

0.5

0.5

0

0

–0.5

–0.5

–1

–1 –1

–0.5

0

0.5

1

(c)

(d)

1.5

1

1 0.5 0.5 0

0

–0.5 –0.5 –1 –1

0

50

100

150

200

250

300

–1.5 –1.5

–1

–0.5

0

0.5

1

Figure 2.14 Effect of symbol timing error on the constellation of the received symbols and channel frequency response: (a) no timing offset, (b) timing offset within CP, (c) rotation of the effective CFR due to timing offset, and (d) timing offset outside CP

1.5

46

Flexible and cognitive radio access technologies for 5G and beyond

2.2.3.3 Sampling clock offset The clock timing difference between the transmitter and receiver causes a sampling clock (or sometimes referred to as sample timing) error. The sampling clock error can be ignored for a small number of subcarriers or a low number of symbols within a given subframe. The sampling error causes carrier and symbol-dependent rotation in the received symbols. When there is a sampling error, the recovered symbols can be related to the transmitted symbols as follows: Ym (k) = Xm (k)e( j2π mkξ Ns /N ) ,

(2.24)

where ξ is the relative clock deviation of the reference oscillator, and Ns = N + Ng with Ng being the number of samples used for the CP. It should be noted that the rotation increases as the carrier and symbol index grow. In other words, the effect of sampling clock error is more in the higher indexed subcarriers and the later symbols of the subframe. Figure 2.15 shows an example of the effect of sampling clock offset in OFDM systems. The sampling clock offset effect increases while moving away from the center carrier (i.e., toward the edge carriers). Also, the average error vector magnitude over all subcarriers increases as the symbol index increases.

Average EVM (%)

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 −100

−80

−60

−40

20

40

60

20 −20 0 Data carrier index

40

60

80

100

140

160

180

200

Average EVM (%)

1 0.8 0.6 0.4 0.2 0

0

80

100 120 Symbol index

Figure 2.15 Effect of sampling clock offset on OFDM subcarriers and symbols

OFDM and alternative waveforms

47

2.2.3.4 Phase noise The phase noise is a random process similar to Doppler spread, and it causes three major problems in OFDM systems: common phase offset, power degradation, and ICI. Assuming an OFDM signal, which is only affected by phase noise (φ(n)) at the receiver, the received time-domain signal can be expressed as follows: ym (n) = xm (n)e jφm (n) .

(2.25)

Assuming that the phase offset is small (i.e., e jφ(n) ≈ 1 + jφ(n)), the recovered symbols is expressed in the following form: N −1 1 φm (n)} Ym (k) ≈ Xm (k) {1 + j N n=0

(2.26)

Common phase term

+

N −1 N −1 j Xm (r) φm (n) · e j(2π/N )(r−k)n . N r=0,r=k n=0 ICI term

The common phase term in (2.26) introduces a rotation to the constellations. This rotation is the same for all subcarriers, and it is representative of the average phase noise. When the phase noise is small, the common phase term is the dominant phase noise effect, and it accumulates over time (i.e., the variance increases over time). The problems associated with this term can be avoided easily using a carefully implemented pilot-based tracking. The last term in (2.26) represents the leakage from neighboring subcarriers (i.e., ICI) and illustrated in Figure 2.16. This term cannot be corrected as both phase offset (φm (n)) and input data sequence (Xm (n)) are random. Therefore, it causes signalto-noise ratio (SNR) degradation of the overall system. The only way to reduce the interference due to this term is to improve the performance of the oscillator with the associated cost increase. The ICI power due to the phase noise can be approximately modeled as follows [24]: πβTs Es EH PICI ∼ , (2.27) = 3 where β represents the two-sided 3-dB bandwidth (i.e., the frequency spacing between 3 dB points of its Lorentzian PSD [24,25]), Ts is the OFDM symbol duration, and Es and EH represent the symbol energy and channel power, respectively, which are often normalized to unity. Accordingly, the signal-to-phase-noise-interference-ratio (SPNIR) can be expressed as follows [24]: SPNIR =

1 − (πβTs /3) . (πβTs /3)

(2.28)

The numerator in the earlier equation includes signal power degradation due to phase noise. It should also be noted that (2.28) assumes no other noise and interference in the system.

48

Flexible and cognitive radio access technologies for 5G and beyond

fc

f1

f2

f3

f4

fc+f1 fc+f2 fc+f3 fc+f4 (a)

fc

f1

f2

f3

f4

fc+f1 fc+f2 fc+f3 fc+f4 (b)

Figure 2.16 Effect of phase noise on OFDM subcarriers: (a) ideal local oscillator and (b) practical local oscillator

PA output PA input

1 dB 10–1

Saturation Probability

Output power (dBm)

100

Backoff Linear region

10–2

Nonlinear region

10–3 Input power (dBm) (a)

0

2

4 6 Power (dB)

8

10

(b)

Figure 2.17 PA characteristics: (a) PA input power versus output power characteristics curve and (b) power CCDF curve of PA input and PA output

2.2.3.5 PA nonlinearities Power amplifiers (PAs) can operate as linear devices only for limited input power, and they distort the transmitted signal beyond a certain input power. The linear and nonlinear operating regions of a PA can be defined with respect to its 1 dB compression point, where the output power of a PA is reduced by 1 dB as shown in Figure 2.17(a). Also, the saturation effect can be clearly seen in Figure 2.17(b) by checking the signal power variance between the PA input and PA output. To minimize its power consumption and

OFDM and alternative waveforms

49

operate at its highest efficiency, a PA is ideally operated close to its saturation point. However, beyond the saturation point, the PA nonlinearity causes several problems, such as amplitude-to-amplitude (AM–AM) distortion, amplitude-to-phase (AM–PM) distortion, spectral regrowth, harmonic distortion, intermodulation distortion, SNR degradation, and modulation inaccuracy. For example, the power back-off technique is widely used in current wireless technologies to remedy the problems due to wide signal dynamic ranges. However, this technique sacrifices efficiency and increases power consumption. On the other hand, baseband linearization techniques are utilized to pre-distort the signal and compensate for the nonlinear effects of PAs. One of the classical and commonly used nonlinear PA models is Saleh’s PA model [26]. It is a simple nonlinear model without memory, and it is defined by only two parameters α and β. The model uses two functions to model the AM–AM and AM–PM characteristics of nonlinear amplifiers. It should be noted that α and β are different for each function. For a given PA, these two parameters (i.e., α and β) can be extracted using a least-squares approximation to minimize the relative error between the target PA measurements and the predictions by the model. The AM–PM equations determining the distortions can be modeled as follows: A(r) =

αA r , 1 + βA r 2

(2.29)

P(r) =

αP r 2 , 1 + βP r 2

(2.30)

and

where r(t) represents the envelope of the PA input signal. The input signal is expressed as follows: Sin (t) = r(t) cos(2πfc t + ψ(t)),

(2.31)

where fc is the carrier frequency and ψ(t) is the phase of the input signal. The output of PA can be derived as follows: Sout = A(r(t)) cos(2πfc t + ψ(t) + P(r(t))).

(2.32)

The impact of PA nonlinearities in OFDM is more significant compared to single carrier systems since OFDM systems have a higher PAPR that requires the PAs to be operated in the linear region. The nonlinear PAs cause distortions to the OFDM signal both in-band and out-of-band, which have a severe impact on the communication performance. To see these distortions, an OFDM signal is generated and passed through PA using the Saleh’s PA model mentioned earlier. In Figure 2.18(a), the effect of PA nonlinearity is shown on the constellation. The constellation is noisy due to the in-band interference created by the PA. Furthermore, Figure 2.18(b) illustrates the spectrum of the PA output. The PA nonlinearities lead to spectral regrowth and out-of-band distortions accordingly for the OFDM signal under test.

Flexible and cognitive radio access technologies for 5G and beyond 0.8

–15

0.6

–20

0.4

–25 Power (dB/Hz)

Quadrature

50

0.2 0 –0.2 –0.4

–30 –35 –40

–0.6

–45

–0.8

–50 –0.5

0 In-phase (a)

0.5

0 dB input power 5 dB input power 7 dB input power 10 dB input power

–55

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Normalized frequency (b)

Figure 2.18 (a) Effect of PA nonlinearities on the constellation of received symbols and (b) effect of PA nonlinearities on the spectrum

To overcome PA nonlinearities, the PAPR of OFDM should be reduced using various techniques such as amplifier back-off and various precoding strategies.

2.2.3.6 IQ impairments One of the major impacts of the in-phase and quadrature (IQ) modulators in OFDM systems is that it introduces interference between the modulated symbols on the image carriers. The image carriers are the carriers, which are equally spaced with respect to the direct current (DC) carrier. For example, for an FFT size of N , if the DC carrier is indexed as the carrier number N /2, the carriers N /2 + k and N /2 − k are called as image carriers, where k is the carrier away from the DC carrier. Therefore, the received signal at every carrier of interest (let us say kth carrier) is dependent on the symbol transmitted on that carrier as well as the symbol transmitted on the opposite image carrier (i.e., −kth carrier). As a result, the image carrier causes interference on the carrier of interest. Depending on the transmission on the image carrier, it causes self or multi-access interference. If the transmission on the image carrier belongs to the same user, it causes self-interference. For example, in a time division multiple access (TDMA) based OFDM system, the type of interference is the self-interference. On the other hand, if the image carrier transmission belongs to another user, it causes multi-access interference. For example, in OFDMA systems, the interference can be both self or multi-access interference. IQ modulator modulates the in-phase I and quadrature-phase Q components with high-frequency carriers. The transmitted signal that goes through the IQ vector modulator experiences several levels of signal distortion due to imperfection in the IQ modulator. The major IQ impairments can be classified as IQ offset, IQ gain imbalance, and IQ quadrature-error. IQ offset, which is also called as IQ origin offset or carrier leakage, indicates the magnitude of the carrier feedthrough. IQ offset can be observed as an offset in the constellation. Gain mismatch or gain imbalance results in

OFDM and alternative waveforms

51

the amplitude of one channel being smaller than the other. The baseband model of IQ origin offset and IQ gain imbalance that are introduced into time-domain transmitted signal can be represented as follows: ym (n) = Is {xm (n)} + jQs {xm (n)} + Iof + jQof ,

(2.33)

where {x} represents the real part of x and {x} represents the imaginary part of x. When Is = Qs , the IQ imbalance becomes zero, and when both Iof and Qof are zero, there is no IQ offset. Quadrature skew error indicates the orthogonal error between the I and Q signals. Ideally, I and Q channels should be exactly orthogonal (i.e., 90◦ apart). When the orthogonality is not ideal, then a quadrature error is observed. For example, the following model shows the quadrature offset that is introduced into the time-domain signal: ym (n) = {xm (n)} + {xm (n)} sin(α) + j (xm (n)) cos(α),

(2.34)

where α is the quadrature error. When α is equal to 0◦ , ym (n) becomes equal to xm (n) (i.e., no quadrature error). The effect of IQ gain imbalance and IQ quadrature error on OFDM is illustrated in Figure 2.19. If the DC carrier, which is not used in various standards, is ignored, the transmitter IQ impairments, excluding the origin offset, can be represented in a unified form in the frequency-domain transmitted samples as follows: Xm (k) Xm (−k) Xˆ m (k) = {Is + Qs e−jα } + {Is − Qs e−jα }. 2 2

(2.35)

As can be seen from the above equation, the transmitted signal after the IQ modulator at the kth carrier depends not only on the symbol that is allocated to the kth carrier,

1.5

1.5

1

1

0.5

0.5 Q

No IQ imbalance

0

With quadrature error No quadrature error

0

Q

With IQ imbalance

–0.5

–0.5

–1

–1

–1.5 –1.5

–1

–0.5

0 I (a)

0.5

1

1.5

–1.5 –1.5

–1

–0.5

0 I (b)

0.5

1

1.5

Figure 2.19 Effect of IQ impairments on OFDM: (a) IQ gain imbalance and (b) IQ quadrature error

52

Flexible and cognitive radio access technologies for 5G and beyond

but also to the symbol that is allocated to the −kth carrier. If these interferences are not handled properly at the receiver, they cause performance degradation. The IQ modulator impairments also cause distortion in the signal AM–PM. However, this can be handled by a proper channel estimator, as the distortion effect can be folded into the effective channel frequency response. It should be noted that it is assumed that the IQ impairments are due to the transmitter IQ modulator. Since the receiver IQ modulator effects are known at the receiver, they can always be precomputed and pre-compensated at the receiver.

2.3 Alternative waveforms Numerous waveforms are proposed considering all the disadvantages of OFDM [1,5–8]. The proposed waveforms provide better flexibility and time–frequency localization using various filtering/windowing approaches and precoding strategies with certain trade-offs. In addition, the external guard interval, which is CP, is suggested to be replaced with a flexible internal guard interval to improve spectral efficiency further and to provide better performance.

2.3.1 Multicarrier schemes 2.3.1.1 Windowed-orthogonal frequency division multiplexing The discontinuity between consecutive OFDM symbols due to the inherent rectangular window in the time-domain results in high OOBE, as discussed earlier. A windowing operation can smoothen these sharp edges to improve the spectral confinement with low complexity. The baseband windowed-OFDM (W-OFDM) [6] can be expressed as follows: xm [n] =

N −1

Xm [k]g[n − m(N + LCP + LExt )]e j2π kn/N

(2.36)

k=0

where Xm [k] is the complex data transmitted on the kth subcarrier and mth W-OFDM symbol, LCP presents the CP length, LExt expresses the windowing extension, and g[n] shows the windowing function. Various windowing functions have been compared thoroughly [27] with different trade-offs between the main lobe width and the sidelobe suppression. The transmitter windowing operation is illustrated in Figure 2.20.

TCP

TOFDM

CP for channel CP for windowing

Postfix for windowing

Overlap with next symbol

Figure 2.20 W-OFDM (transmitter)

OFDM and alternative waveforms

53

Effective CP IFFT

A

Data symbol vector

(subcarrier from 0 to P)

CP insertion and postfix

Left window function

P/S

IFFT Effective CP

[0] ⋮ [ ] [ + 1] ⋮ [ ] [ + 1] ⋮ [ − 1]

B

IFFT

(subcarrier from P+1 to R)

CP insertion and postfix

Inner window function

P/S

IFFT Effective CP

C

IFFT (subcarrier from R+1 to N-1)

CP insertion and postfix

A 0 [0] ⋮ [ ] 0 ⋮ 0 0 0 0

0

0 0 ⋮ 0

⋮

⋮

IFFT

B

⋮

⋮

Right window function

P/S

IFFT

C

⋮

0

⋮

[ + 1] ⋮ [ ] 0 ⋮ 0

0

⋮ ⋮

⋮

IFFT

0 ⋮ 0 0 0 0

⋮ ⋮ ⋮

[ + 1] ⋮ [ − 1]

0

IFFT

⋮

⋮

Figure 2.21 Edge windowing block diagram

Initially, the guard duration is increased with an additional CP and a newly added cyclic suffix. Afterward, the window function is applied to the extended symbol. The transition parts (i.e., ramp-ups and ramp-downs) of adjacent symbols are overlapped to reduce the time-domain overhead emerging from the windowing operation. Furthermore, the windowing operation can be performed at the receiver as well to reduce the interference from other users that are operating in the adjacent bands. Edge windowing [28,29] is a more efficient windowing approach to reduce the high OOBE of CP-OFDM. Although the outer subcarriers have a higher contribution to the OOBE compared to the inner subcarriers, conventional windowing techniques apply the same window for all subcarriers within an OFDM symbol. As a result, either spectral efficiency decreases due to an unnecessary CP allocation for windowing operation or performance degrades due to an insufficient CP allocation for the multipath channel. However, the edge windowing approach applies windows with higher sidelobe suppression capabilities to edge subcarriers while it applies lighter windows to inner ones as shown in Figure 2.21. Since the longer windows decrease the CP length that is allocated to the multipath channel assuming a fixed total CP length, edge subcarriers are allocated to the user equipments that experience shorter delay spread.

2.3.1.2 Filter bank multicarrier Filter bank multicarrier (FBMC) offers a wonderful frequency-domain localization through the use of well-designed pulse shaping filters [27,30]. These flexible filters

54

Flexible and cognitive radio access technologies for 5G and beyond

are applied at the subcarrier level, and they provide adaption to various channel conditions and use cases. There are different FBMC implementations, such as filtered multitone, cosine modulated multitone, and staggered modulated multitone (SMT) [31]. However, SMTW, which is widely known as offset quadrature amplitude modulation (OQAM)-FBMC, is the most popular approach due to its ability to handle interference while allowing dense symbol placement in the time–frequency lattice [5]. OQAM signaling provides a staggering of IQ-phase components in both time and frequency domains as shown in Figure 2.22, and the orthogonality is maintained within the real and imaginary domains separately. The baseband FBMC-OQAM is expressed as follows: xm [n] =

N Xm [k]g n − m e j2π kn/N e jφm,k 2 k=0

N −1

(2.37)

where g represents the prototype filter, φm,k is an additional phase term at subcarrier k and symbol index m, which is expressed as (π/2)(m + k). Xm [k] is real valued as the real and imaginary parts are transmitted with a delay. Also, to address a perfect reconstruction of symbols, the prototype filter must satisfy the orthogonality condition [33]. A block diagram of the conventional FBMC-OQAM transmitter and receiver is shown in Figure 2.23. The subcarriers are well localized in the frequency domain due to the use of prototype filters, and they are spread over only a few subcarriers in FBMC systems. As a result, no more than one subcarrier is required as a guard band for

Δf

Δf

T

T

OQAM

QAM In-phase Quadrature-phase

Figure 2.22 QAM signaling versus OQAM signaling [32]

OFDM and alternative waveforms X

0

g[n]

X

0

g[n − ]

g[n]

2

1

g[n]

X

1

g[n − ]

2

IFFT & P/S

2

S/P & FFT

X

0

g[n]

X

1

g[n − ]

X

2

⋮

0

g[n − ] 2

2

X

X

55

1

⋮ ( − 1)

X

− 1

g[n]

X

− 1

g[n − ]

2

( − 1)

2

g[n]

X

g[n − ]

2

2

(a)

− 1

X

− 1

(b)

Figure 2.23 OQAM-FBMC block diagram: (a) transmitter and (b) receiver

non-orthogonal transmissions [5], and they achieve high spectrum efficiency. Furthermore, the frequency-domain localization provides immunity to the Doppler effects, and FBMC systems are suitable for high-mobility applications as well. On the other hand, there exist several practical challenges. The MIMO integration and pilot design with OQAM-FBMC are not as simple as in CP-OFDM due to the intrinsic interference resulting from OQAM signaling [18].

2.3.1.3 Generalized frequency division multiplexing Generalized frequency division multiplexing (GFDM) [34] also applies subcarrierwise filtering similar to FBMC. However, the filters for pulse shaping are circularly convoluted over a defined number of symbols. The symbols are processed block-wise, and a CP is appended to this block. Considering N subcarriers in each subsymbol group and M subsymbol group in each block, a GFDM symbol is represented as follows: xm [n] =

M −1 N −1

Xm,s [k]gm,s,k [(n − sN ) mod (MN )]e j2π kn/N

(2.38)

s=0 k=0

where Xm,s [k] is the complex data transmitted on the kth subcarrier, sth subsymbol, and mth GFDM symbol, and g shows the prototype filter. There is no orthogonality constraint on GFDM prototype filters, and GFDM is usually a nonorthogonal transmission scheme with nonorthogonal filters. A block diagram of the conventional GFDM transmitter is shown in Figure 2.24.

56

Flexible and cognitive radio access technologies for 5G and beyond ,0 [0]

gm,0,0 [n mod(MN)]

⋮

⋮

, − 1 [0]

⋮

,0 [

⋮

gm,M-1,0[n-(M−1)N mod(MN)]

P/S

⋮

−1]

⋮

gm,0,N-1 [n mod(MN)] ⋮

⋮

exp[0]

⋮

−1]

⋮ , −1[

exp[0] ⋮

⋮

⋮

⋮

∑

⋮

P/S

CP insertion

TX signal

exp[j2πnN−1/N] ⋮

⋮ gm,M-1,N-1 [n-(M−1)N mod(MN)]

⋮

⋮

exp[j2πnN−1/N ]

Figure 2.24 GFDM block diagram (transmitter)

TX data

S/P

⋮

N-FFT

⋮

Windowing

⋮

TX data

S/P

⋮

N-FFT

⋮

Windowing

⋮

M blocks

MN-IFFT ⋮

TX data

S/P

⋮

N-FFT

⋮

Windowing

P/S ⋮

⋮

⋮

CP insertion

TX signal

⋮

Figure 2.25 Equivalency of GFDM and DFT-s-OFDM (transmitter)

GFDM is proposed as a flexible waveform where the number of subsymbols, subcarriers, and prototype filters are adjustable for various channel conditions and use cases. Conceptually, a GFDM signal can also be generated with M FFTs of size N , filter banks, and an MN -point IFFT, as shown in Figure 2.25. From this implementation perspective, it is equivalent to a discrete Fourier transform spread OFDM (DFT-s-OFDM) signal when the rectangular function is used as a prototype filter, which also explains lower PAPR compared to CP-GFDM. This equivalency is further discussed in the following single-carrier waveform discussion. Also, it is equivalent to CP-OFDM when M equals 1. GFDM shares the well-frequency-localized characteristic with OQAM-FBMC. Hence, it is suitable for high-mobile scenarios and provides more immunity to synchronization errors. Although GFDM offers flexibility in the waveform, the non-orthogonal transmission scheme requires complex successive interference cancelation (SIC) algorithms at the receiver. Similar to OQAM-FBMC, pilot design and MIMO transmission are complicated. Furthermore, the block-wise transmission

OFDM and alternative waveforms

57

causes latency that makes it infeasible for low-latency applications. Finally, the waveforms that perform subcarrier-wise filtering, namely, FBMC and GFDM, require a new transceiver design, and there is no backward compatibility with 4G LTE.

2.3.1.4 Universal filtered multicarrier UFMC [35] applies subband-wise filtering to reduce OOBE. The subband-wise filtering is considered as a compromise between the whole-band filtering and subcarrier-wise filtering. Hence, the filters are shorter compared to the FBMC, where the length of filters is much longer than the symbol duration. The total available bandwidth is partitioned into subbands, and filtering is performed with a fixed frequency-domain granularity [5]. Considering B subbands (blocks) in total and using a filter of length Lf , the baseband UFMC signal is represented as follows: xm [n] =

f −1 N −1 B−1 L

Xm,b [k]gb [l]e j2π n(k−l)/N

(2.39)

b=0 l=0 k=0

where Xm,b [k] is the complex data transmitted on the kth subcarrier, bth subband, and mth UFMC symbol, and g[l] shows the frequency equivalent windowing function of a time-domain finite impulse response (FIR) filter. Therefore, each block length is Lf + N − 1. The use of CP is optional to provide better immunity against ISI, and it is also called as universal filtered OFDM (f-OFDM) when CP is used. However, typical UFMC systems do not utilize CP, and the transitions regions (i.e., ramp-ups and ramp-downs) provide soft ISI protection. A block diagram of the conventional UFMC transmitter is shown in Figure 2.26. The symbols are sent back-to-back without any overlapping, and hence orthogonality in time is maintained. However, a more complicated receiver is required due

TX data of block #0

S/P

⋮

N-IFFT

⋮

Pad Lf −1 zeros

⋮

Filter gb[l] with length

TX data of block #2

S/P

⋮

N-IFFT

⋮

Pad Lf −1 zeros

⋮

Filter gb[l] with length

⋮

⋮

S/P

⋮

Lf

Lf

⋮

⋮

∑

TX data of block #B-2

⋮

N-IFFT

⋮

⋮

⋮

Pad Lf −1 zeros

⋮

Filter gb[l] with length Lf

P/S ⋮

⋮

Figure 2.26 UFMC block diagram (transmitter)

TX signal

58

Flexible and cognitive radio access technologies for 5G and beyond

to the lack of CP [36]. A conventional UFMC receiver utilizes an FFT block that has twice the size of IFFT block at the UFMC transmitter. UFMC provides a better localization in the frequency domain and robustness against time–frequency offsets compared to CP-OFDM. Also, shorter filter lengths compared to subcarrier-wise filtering make it more suitable for low-latency applications. On the other hand, these shorter filters offer limited OOBE suppression. Furthermore, increased complexity due to the lack of CP and complicated filtering operations should be dealt with intelligently to design efficient communications systems.

2.3.1.5 Filtered-orthogonal frequency division multiplexing F-OFDM [37] is another subband-wise filtered multicarrier scheme, but the filtering granularity is more flexible than UFMC. This flexibility makes f-OFDM more suitable for asynchronous transmissions, such as different numerologies, compared to UFMC, with the cost of increased complexity. Considering B blocks in total, the baseband f-OFDM signal is represented as follows: xm [n] =

f ,b −1 Nb −1 B−1 L

b=0

l=0

Xm,b [k]gb [l]e j2π n(k−l−bLCP )/Nb

(2.40)

k=0

where Xm,b [k] is the complex data transmitted on the bth block and kth subcarrier, gb [l] shows the frequency equivalent windowing function of a time-domain FIR filter on the bth block, and LCP presents the CP size. As can be seen in the equation, f-OFDM maintains the CP in contrast to UFMC. Therefore, it is more immune to the ISI and needs a less complex receiver. Ideally, the frequency-domain window gb [l] is desired to be a rectangle with a size of Lf ,b . However, it corresponds to an infinite length sinc shape response in the time domain, and hence it is impractical. Therefore, windowed sinc functions are used in the filtering operation. More details on various filters can be found in [27]. A block diagram of the conventional f-OFDM transmitter is shown in Figure 2.27. Matched filtering and

TX data of block #0

TX data of block #2

S/P

S/P

⋮

⋮

N0-IFFT

N1-IFFT

⋮

⋮

CP0 insertion

CP1 insertion

⋮

⋮

Filter g0[l] with length Lf,0

Filter g1[l] with length Lf,1

⋮

⋮

∑

TX data of block #B−1

⋮

⋮

S/P

⋮

⋮

NB−1- IFFT

⋮

⋮

⋮

CPB−1 insertion

⋮

Filter g B−1[l] with length Lf,B−1

P/S

⋮

⋮

Figure 2.27 f-OFDM block diagram (transmitter)

TX signal

OFDM and alternative waveforms

59

identically sized IFFT/FFT blocks at the receiver also differ f-OFDM from the other subband-wise filtering technique, UFMC. The f-OFDM shares all advantages of well-frequency-localized waveforms such as low OOBE, allowing asynchronous transmission, and requiring a less number of guard tones. Although f-OFDM cannot provide low OOBE as subcarrier-wise filtered multicarrier schemes due to the use of shorter filter lengths, it is compatible with MIMO transmission scheme and does not require any SIC algorithm. However, complexity is still the main drawback of f-OFDM compared to CP-OFDM.

2.3.2 Single-carrier schemes 2.3.2.1 CP-DFT-s-OFDM DFT-s-OFDM is used in the uplink of the 4G LTE because of its lower PAPR characteristic compared to OFDM. The data input can be considered as independent and identically distributed random variables as discussed earlier. Accordingly, the corresponding IDFT output of CP-OFDM has a high variance. This high variance can be mitigated by providing a correlation to the input with a DFT operation prior to the IDFT process, as shown in Figure 2.28. Also, the utilization of CP in DFT-s-OFDM ensures the circularity of the channel and enables easy FDE to handle the multipath channel effect. This waveform can be interpreted in two ways [7]. One interpretation is that it is a precoded CP-OFDM scheme, where PAPR is mitigated by a DFT precoding operation. This interpretation provides researchers to consider different precoding methods. The other interpretation is that it is a transmission scheme that upsamples the input data by the ratio of the IDFT and DFT block sizes (i.e., N /M , where N > M ) and performs a circular pulse shaping with a Dirichlet sinc function. This interpretation leads designers to take different pulse shaping strategies into account to reduce the PAPR further and control the OOBE. 0 ⋮ 0 TX data

S/P

⋮

M-DFT

⋮

N-IDFT

⋮

P/S

CP insertion

TX signal

⋮

S/P

CP removal

RX signal

0 ⋮ 0 (a)

RX data

P/S

⋮

M-IDFT

Discard

⋮

⋮

⋮

FDE

Discard

N-DFT

⋮ (b)

Figure 2.28 CP-DFT-s-OFDM block diagram: (a) transmitter and (b) receiver

60

Flexible and cognitive radio access technologies for 5G and beyond

The low complexity, support of dynamic spectrum access, and MIMO compatibility features make CP-DFT-s-OFDM a favorable waveform, similar to the CP-OFDM. However, the spectral efficiency of this waveform is also comparable to CP-OFDM and suffers from high OOBE due to the discontinuity between adjacent symbols. Hence, similar windowing and filtering approaches as discussed in the multicarrier schemes discussion can be utilized to increase spectral efficiency.

2.3.2.2 ZT-DFT-s-OFDM The guard interval is hard-coded in 4G LTE systems, and there are only two options as normal and extended CP. However, the base station is preset to only one of these guard intervals to keep the number of symbols per frame fixed. The use of different guard intervals leads to the generation of mutual asynchronous interference even when the frames are aligned [8]. Hence, the users with two different CP durations do not coexist in the same cell. As a result, the nonflexible guard interval penalizes the user equipments that experience better channel conditions. Zero-tail (ZT)-DFT-s-OFDM [38] is proposed to solve this problem. The CP is replaced with an internal guard period that provides the same functionality. The total period of the guard duration and data duration is fixed, but the ratio between them is flexible. The flexibility provides better spectral efficiency while maintaining the total symbol duration. Zero vectors with variable lengths are inserted into the head and tail of the data before the DFT operation in this approach. The tail length is set to be longer than the delay spread of the channel and, hence the leakage to the next symbol does not have significant power. Also, the zeros in the head, which are usually shorter than the zeros at the tail, provide a smoother transition and yield substantial OOBE reduction [8]. A block diagram of the conventional ZT-DFT-s-OFDM transmitter and receiver is shown in Figure 2.29. 0 ⋮ 0

zh zeros at head TX data

S/P

⋮

⋮

M-DFT

zt zeros at tail

N-IDFT

⋮

P/S

TX signal

N-DFT

⋮

S/P

RX signal

0 ⋮ 0 (a)

(a) Discard

⋮

⋮

FDE

⋮

Discard

⋮

Discardzh zeros RX data

P/S

⋮

M-IDFT

Discard zt zeros

(b)

Figure 2.29 ZT-DFT-s-OFDM block diagram: (a) transmitter and (b) receiver

OFDM and alternative waveforms

61

The fixed sequences (i.e., zero vectors) appended to each symbol ensure circularity at the receiver, and hence ZT-DFT-s-OFDM supports single-tap FDE. However, the residual energy of the data part in the last samples introduces a noncyclical leakage to the next symbol [8]. Therefore, the internal guard interval approach does not provide perfect channel circularity as CP does. Furthermore, this leakage is a limiting factor in the link performance for the users utilizing high-order modulations in a multipath environment. The PAPR and OOBE are low for ZT-DFT-s-OFDM, and spectral efficiency is increased due to the flexible guard interval. The internal guard feature makes it suitable for different symbol durations without introducing mutual asynchronous interference. However, this flexibility causes extra overhead to track the delay spread of the channel.

2.3.2.3 UW-DFT-s-OFDM Unique word-DFT-s-OFDM (UW DFT-s-OFDM) [39] is another single-carrier scheme that utilizes a flexible internal guard interval. The ZTs and heads of the ZTDFT-s-OFDM are replaced with a fixed sequence in this approach. Since the fixed sequence is inserted before the DFT operation, as shown in Figure 2.30, the orthogonality is provided between the data and the UW. The channel circularity is also ensured with this waveform, and as a result, simple FDE is supported. However, the leakage from the data part limits its link performance for the high-order modulations similar to ZT-DFT-s-OFDM. Different from the ZT part, UW can also be exploited for synchronization and channel tracking purposes [40]. Therefore, it improves spectral efficiency. The OOBE leakage characteristics of this waveform are comparable to ZT-DFT-s-OFDM, which also uses a flexible internal guard interval. Adding data-dependent “perturbation” signal or modifying the kernel function with windowing the input data (which is

0 ⋮ 0 S/P

TX data

⋮

⋮

M-DFT S/P

UW

N-IDFT

⋮

P/S

TX signal

⋮

S/P

RX signal

⋮ 0 ⋮ 0 (a)

RX data

S/P

⋮

S/P

⋮

M-IDFT UW removal

Discard

⋮

⋮

⋮

FDE

Discard

N-DFT

⋮ (b)

Figure 2.30 UW-DFT-s-OFDM block diagram: (a) transmitter and (b) receiver

62

Flexible and cognitive radio access technologies for 5G and beyond

analogous to GFDM with UW) mitigates the OOBE and PAPR further [7]. However, these benefits come with increased complexity.

2.4 Discussion The frequency localization is important to allow asynchronous transmission across adjacent subbands and coexistence with other waveforms. On the other hand, the time localization is critical for low-latency applications where longer filter/window durations are not feasible for URLLC. All discussed alternative waveforms offer lower OOBE compared to CP-OFDM and its single-carrier equivalent, that is, CP-DFT-sOFDM. The subcarrier-wise filtering operation in FBMC results in the best frequency localization among these alternative waveforms due to the use of longer filter lengths in the time domain. Although GFDM is another subcarrier-wise filtered waveform, the rectangular window shape in the time domain causes abrupt transitions and increases its OOBE. However, windowing can be performed on this waveform, and W-GFDM presents good spectral confinement as well. On the other hand, relatively shorter filters in the subband-wise filtered waveforms lead to better time localization with the price of increasing OOBE compared to the subcarrier-wise filtered waveforms. Most multicarrier schemes suffer from high PAPR and are not suitable when high-energy efficiency is required. However, GFDM exhibits a reduced PAPR characteristic due to its equivalency to DFT-spread waveforms, as discussed before. The single-carrier schemes are preferable in energy-limited use cases along with the use of flexible guard intervals that provide better spectral confinement and improved PAPR. The spectral efficiency is another critical design criterion that is profoundly affected by the window/filter duration, the shape of the filter, and extra overheads. Well-frequency-localized waveforms reduce the need for guard bands and hence leading to better efficiency in the frequency domain. On the other hand, the waveforms that do not utilize a guard interval, such as FBMC, are expected to have higher efficiency in the time domain. However, BER/block error rate (BLER) performance decreases substantially in a multipath fading channel due to the lack of guard interval. As a result, complex receivers are required since an easy FDE is not possible. MIMO compatibility is also essential to achieve high throughput. The schemes that allow interference, such as FBMC and GFDM, cannot deploy straightforward MIMO algorithms. Finally, the guard interval in the time domain makes a waveform more robust against ISI and time-offset errors. In addition, the guard bands or the use of welllocalized waveforms in the frequency domain make a waveform robust against carrier frequency offset and Doppler effects that reduce ICI and adjacent channel interference in a multiple access environment. As a result, FBMC has the best immunity to ICI and is the most vulnerable to ISI. A summary of the main advantages/disadvantages of these alternative waveforms is provided in Table 2.1.

OFDM and alternative waveforms

63

Table 2.1 Waveform comparison Multicarrier Schemes Waveform

Advantages

• • CP-OFDM • •

Disadvantages • • • •

Simple FDE Easy MIMO integration Flexible frequency assignment Low implementation complexity

High OOBE and PAPR Strict synchronization requirement Poor performance for high mobility applications Hard-coded CP

• All advantages belong to CP-OFDM • Lower OOBE compared to CP-OFDM

• Either poor spectral efficiency or BER performance (depending on windowing type/parameters)

OQAMFBMC

• • • •

• • • •

GFDM

• Flexible design • Good frequency localization • Reduced PAPR

UFMC

• Good frequency localization • Shorter filter length compared to subcarrier• No immunity to ISI due to lack of CP wise operations (i.e., OQAM-FBMC and GFDM) • High receiver complexity due to increased FFT size • Compatible with MIMO

W-OFDM

Best frequency localization (i.e., lowest OOBE) Good spectral efficiency (no guard band or CP) Suitable for high-mobility applications Convenient for asynchronous transmission

Challenging MIMO integration and pilot design No immunity to ISI due to lack of CP High implementation complexity Increased power consumption due to OQAM signaling

• Higher latency due to block processing • Challenging MIMO integration and pilot design • High implementation complexity

• Flexible filtering granularity • Better frequency localization • Very high implementation complexity F-OFDM • Shorter filter length compared to subcarrierwise operations (i.e., OQAM-FBMC and GFDM) • Compatible with MIMO Single-carrier Schemes CP-DFT-s- • All advantages belong to CP-OFDM OFDM • Low PAPR

• High OOBE • Strict synchronization requirement • Hard-coded CP

• Flexible guard interval ZT-DFT-s• Better spectral efficiency OFDM • Lower OOBE compared to CP-DFT-s-OFDM

• Strict synchronization requirement • Extra control signaling • Limited link performance for higher order modulation

• Flexible guard interval UW-DFT-s• Best spectral efficiency OFDM • Lowest OOBE and PAPR

• • • •

Strict synchronization requirement Extra control signaling Limited link performance for higher order modulation High implementation complexity

2.5 Conclusion In this chapter, the OFDM waveform is discussed in detail, along with its performance in the presence of a multipath channel and various impairments. Also, major alternative waveforms are presented, and associated trade-offs are summarized. It could be concluded that there is no waveform that fits all requirements of diverse services yet, and the coexistence of different flexible waveforms should be considered for next-generation communication systems as detailed in Chapter 4. Unlike the previous standards, future standards will support high flexibility to fully exploit and further increase the potential of future communications systems.

64

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Flexible and cognitive radio access technologies for 5G and beyond Michailow N, Matthé M, Gaspar IS, et al. Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks. IEEE Transactions on Communications. 2014;62(9):3045–3061. Vakilian V, Wild T, Schaich F, et al. Universal-filtered multi-carrier technique for wireless systems beyond LTE. In: 2013 IEEE GLOBECOM Workshops (GC Wkshps), Atlanta, GA; 2013. p. 223–228. Schaich F, Wild T, and Chen Y. Waveform contenders for 5G – suitability for short packet and low latency transmissions. In: 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), Seoul; 2014. p. 1–5. Zhang X, Jia M, Chen L, et al. Filtered-OFDM – enabler for flexible waveform in the 5th generation cellular networks. In: 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA; 2015. p. 1–6. Berardinelli G, Tavares FML, Sorensen TB, et al. Zero-tail DFT-spreadOFDM signals. In: 2013 IEEE GLOBECOM Workshops (GC Wkshps); 2013. p. 229–234. Sahin A, Yang R, Ghosh M, et al. An improved unique word DFT-spread OFDM scheme for 5G systems. In: 2015 IEEE GLOBECOM Workshops (GC Wkshps); 2015. p. 1–6. Coon J, Sandell M, Beach M, et al. Channel and Noise Variance Estimation and Tracking Algorithms for Unique-Word Based Single-Carrier Systems. IEEE Transactions on Wireless Communications. 2006;5(6):1488–1496.

Chapter 3

Mixed numerology OFDM and interference issues Abuu B. Kihero1 , Muhammad Sohaib J. Solaija1 , and Hüseyin Arslan1,2

The upcoming generations of wireless communications are characterized by a wide variety of applications. This necessitates a provision of flexible radio access technologies (RATs) capable of adapting to changing user (and network) requirements. One of the most significant development in this regard is the introduction of mixed or multiple numerology concept for the fifth generation (5G) of wireless communication. However, flexibility offered by the mixed numerology concept comes with its own set of challenges, including the novel type of interference referred to as inter-numerology interference (INI). This chapter looks at the various implementation-related issues for 5G’s mixed numerology systems such INI in particular. In addition to discussing the analytical models of INI, mechanisms for its mitigation are also described. Moreover, the generalization of numerology concept to other domains is briefly explored for future communication networks.

3.1 Introduction Unlike the preceding generations of wireless technology, vision of the forthcoming generations (i.e., 5G and beyond) is much more than the mere evolution of broadband services. For example, the imminent 5G technology envisages a more diverse network with seamless coverage, higher data rates, massive connectivity, and significantly higher reliability than any of the earlier generations. As summarized in the previous chapter, prospective use cases to be supported by 5G are divided into three major groups, i.e., enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). The requirements and priorities of these service groups are highly diverse and conflicting with each other, necessitating multiple magnitudes of improvement in the legacy fourth generation (4G) wireless technology and infrastructure. For instance, mMTCbased applications prefer longer symbol duration (i.e., smaller subcarrier spacing) to support delay-tolerant devices, whereas URLLC-based use cases such as vehicleto-vehicle communication require larger subcarrier spacing to satisfy their stringent 1 2

Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Turkey Department of Electrical Engineering, University of South Florida, Tampa, USA

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Flexible and cognitive radio access technologies for 5G and beyond

low-latency requirement as well as increase their immunity against Doppler spread. To embrace such conflicting service requirements, the standardization bodies devised a flexible frame structure through a mixed numerology concept for the 5G-RAT. In a broad sense, numerology refers to the waveform selection/parameterization to form resource blocks based on the user requirements and channel conditions [1]. That is, different users within the same frame are allowed to utilize different waveforms or the same base/core waveform with different parameterizations that best meet their requirements. Based on this definition, implementation of multi-numerology frame structure can be principally categorized into two: hybrid/mixed waveform-based and single waveform-based mixed numerologies. With single waveform approach, different requirements of the users are met by appropriately assigning different values to the controllable parameters of a given base waveform. However, from a wider perspective, defining mixed numerology in terms of a single-base waveform can still be a limitation for the flexibility. For instance, in order to enhance efficiency of the spectrum utilization, there has been an increased effort on enabling coexistence of different communication technologies which fundamentally utilize different waveforms. Consider an example of frequency modulated continuous wave (FMCW) and orthogonal frequency division multiplexing (OFDM) waveforms coexistence for joint radar and cellular communication system. It is therefore clear that a radio access framework supporting mixed waveforms will be required in the near future, making the mixed waveform-based numerology design more forward compatible. Nevertheless, 5G standard has opted the single-waveform-based numerology structure whereby an OFDM has been selected as the parent waveform whose numerologies are defined by subcarrier spacing (SCS) options as specified inTable 1.7 in Chapter 1. Therefore, in the context of 5G-RAT, numerology is defined as a selection of SCS of the OFDM waveform based on a number of factors such as application to be supported, channel condition, and frequency band of operation (i.e., sub-6 GHz or millimeter wave). Note that, although the concept of mixed numerology is compatible with any waveform, 5G-RAT has opted to use OFDM with different SCS options mainly because of the OFDM’s maturity and the immense effort that is already put into its standardization for the previous generation (i.e., Long Term Evolution (LTE)). It can be intuitively perceived how different SCSs can be employed to address different demands of each service class of 5G. For instance, small SCSs are more suitable for mMTC, since they can support a higher number of simultaneously connected devices within the same bandwidth and require lower power, intermediate SCSs are appropriate for eMBB which requires both high data rate and significant bandwidth, and larger SCSs are more suitable for delay-sensitive applications pertaining to the URLLC service due to their shorter symbol duration. However, the system flexibility achieved through such a mixed numerology frame structure comes at a cost of a newly introduced form of interference termed as INI [2]. INI comes into the picture as a result of the inherent non-orthogonality existing between subcarriers with different SCSs. Due to the lack of orthogonality, side lobes of the subcarriers of one numerology tend to cause interference to the adjacent subcarriers of different numerologies as illustrated in Figure 3.1. Basically, 5G technology has limited itself to the multiplexing of numerologies in frequency domain where INI problem exists. It is worth noting that it is also possible to multiplex OFDM

Mixed numerology OFDM and interference issues

69

Frequency (a)

Frequency (b)

Figure 3.1 Numerology multiplexing in frequency domain: (a) conventional single numerology system—orthogonality is maintained and (b) different numerologies multiplexing—orthogonality is lost

Ts

Ts

Ts Time

(a)

Ts

1

Ts

Ts

2

3

Time (b)

Figure 3.2 Numerology multiplexing in time domain: (a) conventional single numerology—orthogonality is maintained and (b) different numerologies—orthogonality is maintained numerologies in time domain as shown in Figure 3.2 [3]. The fact that OFDM waveform is well-localized in time domain ensures that aligning different numerologies in time domain can maintain the orthogonality between blocks. However, multiplexing numerologies in frequency is preferred due to its better backward and forward

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Flexible and cognitive radio access technologies for 5G and beyond

compatibility as well as its inclusive support for latency-sensitive use cases compared to the time domain counterpart [4]. Extension of the multi-numerology concept to other domains will be covered in detail in the next chapter. While detailed explanation of the standardized 5G mixed numerology frame structure is already given in Chapter 1, this chapter focuses on providing an indepth analysis of the INI problem and identifying various areas through which mixed numerology structure can be improved for the future generations.

3.2 Mixed numerology multiplexing Apart from the loss of orthogonality among subcarriers as described in the introduction, mixing different numerologies in frequency domain also causes difficulty in achieving perfect symbol alignment in time domain. With the same sampling rate, an OFDM symbol of one numerology does not perfectly align with the symbol of another numerology, which makes synchronization within the frame difficult. However, with the scalable numerology design adopted by 5G, symbol duration of one numerology is always an integer multiple of the symbol duration of another numerology. Therefore, multi-numerology symbols can be perfectly aligned over the so-called least common multiplier (LCM) symbol duration, TLCM [5]. For instance, given two numerologies with SCSs f1 and f2 which are related by f2 = Q × f1 implies that their respective symbol durations Ts1 and Ts2 obey the relationship Ts1 = Q × Ts2 . This connotes that Q symbols of the second numerology can be perfectly aligned with one symbol of the first numerology. However, such a perfect symbol alignment is only possible for a well-synchronized frame, which might not always be the case. For simplicity and easy understanding of the fundamental concepts during INI analysis, we first consider a well-synchronized mixed numerology system with only two users, UE-1 and UE-2, utilizing numerology-1, with narrow subcarriers (i.e., small subcarrier spacing f1 ) and numerology-2, with relatively wider subcarriers (i.e., larger subcarrier spacing f2 ), respectively. Nevertheless, the intuition and analysis that will be developed herein can easily be extended and applied to any number of multiplexed numerologies by, for example, considering one pair of numerologies at a time. Let the considered narrow subcarrier numerology (NSN) and wide subcarrier numerology (WSN) share the system’s normalized bandwidth B in the ratio η1 and η2 , where η1 + η2 = 1 (i.e., no guard band (GB) between them). Complying with the 5G standards, the ratio f2 /f1 = Q is always an integer power of 2. Note that Q = 1 refers to the case in which UE-1 and UE-2 use the same numerology, corresponding to the LTE frame structure. Based on this system setup, in the following subsections, a step-by-step implementation of the mixed numerology is discussed from both frequency and time domain perspectives for downlink transmission.

3.2.1 Frequency domain Figure 3.3 summarizes the multi-numerology multiplexing process in frequency domain, time domain symbol alignment as well as creation of the numerologies’ composite signal for transmission. It features subcarrier mapping blocks right after the conventional serial-to-parallel (S/P) blocks for properly mapping the NSN and

Mixed numerology OFDM and interference issues

71

Xnr (k) subcarrier mapping

~

NSN

Xnr

S/P

0 1 2

0 1 2 3

η1 . N−1 N-point

P/S

xnr

CP-addition

Tx

IFFT Zeros

N−1 0 1

subcarrier mapping

~

WSN

Xwd

S/P

Zeros

2 3

1−η2 . M

M−1

M-point IFFT

P/S

xwd

CP-addition

Shift by: q . TLCM Q Aligning the symbols

M−1

Xwd (l) Time domain

Frequency domain

Figure 3.3 Downlink mixed numerology multiplexing at the transmitter WSN subcarriers into their respective bandwidth parts. Specifically, localized subcarrier mapping technique is implemented at the subcarrier mapping blocks whose outputs can be expressed as X˜ nr , 0 ≤ k ≤ η1 N − 1 Xnr (k) = , (3.1) 0, η1 N ≤ k ≤ N − 1 and

Xwd (l) =

0, X˜ wd ,

≤ l ≤ (1 − η2 )M − 1 , (1 − η2 )M ≤ l ≤ M − 1

(3.2)

where X˜ nr and X˜ wd are input complex modulated symbols of NSN and WSN users, respectively. Xnr (k) and Xwd (l) are symbols on the kth and lth subcarriers of NSN and WSN, respectively, after subcarrier mapping process. N and M are fast Fourier transform (FFT) sizes of NSN and WSN, respectively, such that N = Q × M .

3.2.2 Time domain The output of the inverse fast Fourier transform (IFFT) and parallel-to-serial (P/S) blocks in Figure 3.3 are time domain symbols xnr and xwd of NSN and WSN, respectively. Mathematically, η1 N −1 1 Xnr (k)ej2π nk/N , xnr (n) = IFFT{Xnr }|N -point = √ N k=0

0 ≤ n ≤ N − 1, (3.3)

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Flexible and cognitive radio access technologies for 5G and beyond

and M −1 1 xwd (m) = IFFT{Xwd }|M -point = √ Xwd (l)ej2π ml/M , M l=η1 M

0 ≤ m ≤ M − 1. (3.4)

Note that, if we are to observe the WSN subcarriers with the granularity of the NSN subcarriers, the l-indices with active WSN subcarriers in (3.2) can be defined as l = η1 M + k/Q for {0 ≤ k ≤ η2 N − 1 : k/Q ∈ Z}. Consequently, (3.4) can be rewritten as η2 N −1 1 xwd (m) = √ Xwd (η1 M + k/Q)e(j2π /N )m(k+η1 N ) , 0 ≤ m ≤ M − 1. M k=0

(3.5) Following cyclic prefix (CP) additions to the NSN and WSN symbols, ranges of their time domain samples indices n and m are redefined to −NCP ≤ n ≤ N − 1, and −MCP ≤ m ≤ M − 1, respectively, where NCP and MCP are their corresponding CP durations (in samples), obeying the relationship NCP = Q × MCP in accordance with the 5G standards. As briefly explained earlier, the 5G’s scalable numerology structure solves the symbols alignment issue by facilitating perfect symbols alignment over TLCM duration. TLCM is always equal to the symbol duration of the numerology with the smallest f in a given system. For the full assessment of the system performance, it is necessary to consider performance over at least one complete LCM symbol. However, in practical implementation when TLCM is very large, taking one LCM symbol for joint processing may significantly increase the processing complexity. In such circumstances, it is possibly better to ignore the perfect alignment of the symbols and consider the frame as an asynchronous one [5]. A typical symbol alignment over TLCM duration is illustrated in Figure 3.4, where 0 ≤ q ≤ Q − 1 are indices of the Q concatenated WSN symbols within one TLCM duration. Note that, with the exception of the extended CP option for the f = 60 kHz NCP

N

q=0 q=1 q = Q −11 Q (M + MCP) M + MCP 2(M + MCP) One LCM symbol (TLCM) = NSN

= WSN

N + NCP

Composite signal = CP

Figure 3.4 Mixed numerology symbol alignment and creation of the composite signal at the transmitter

Mixed numerology OFDM and interference issues

73

numerology, all standardized numerologies use the same CP ratio which guarantees that mixed numerologies symbol alignment over TLCM duration holds even after CP addition. For the downlink, the aligned symbols are summed up in time domain to create a composite signal before being converted into RF signal for transmission. WSN signal, xWSN , formed by concatenating Q WSN symbols is modeled as an (M + MCP )-point periodic extension of xwd , given as xWSN =

Q−1

xwdq (m − q(M + MCP )),

(3.6)

q=0

and the NSN signal, xNSN , within a TLCM duration is given by xNSN = xnr .

(3.7)

Finally, the composite signal x to be transmitted is obtained as x = xNSN + xWSN .

(3.8)

3.3 Inter-numerology interference modeling We have mentioned in the introduction that inter-numerology interference (INI) is merely due to the lack of orthogonality between subcarriers with different f s. However, it is important to emphasize that apart from the inherent non-orthogonality between subcarriers, some transmitter and receiver processing of the multinumerology signal create another layer of non-orthogonality between the subcarriers that greatly impact the structure of the observed INI. The following sections intuitively highlight this fact. At the transmitter, INI is due to the inherent lack of orthogonality between subcarriers of different numerologies. This can be observed right after multiplexing of the two numerologies, as visualized in Figure 3.5. For the specific case of 5G with

NSN

WSN

Frequency

Figure 3.5 Inherent non-orthogonality between NSN and WSN subcarriers observed at the transmitter (without CPs in time domain). Interference is well structured at this stage such that only locations encircled in red experience INI.

74

Flexible and cognitive radio access technologies for 5G and beyond

Received composite signal NSN WSN

(a)

(b)

Figure 3.6 FFT window capturing a desired signal from the composite signal: (a) at NSN receiver and (b) at WSN receiver

its scalable numerology structure, the INI created at the transmitter is well structured and it affects only some specific set of NSN subcarrier as shown in Figure 3.5. Only NSN subcarriers that occupy indices that are integer multiple of Q are the victims of INI at this stage. Note that the INI pattern visualized in Figure 3.5 is valid only when there is no CP appended to the time domain symbols of the multiplexed numerologies. The CP addition process to the symbols of each numerology adds another layer of non-orthogonality that cannot be recovered even after CP removal at the receiver.∗ However, as we will see later in Section 3.5, with a more sophisticated CP configuration, non-orthogonality due to CP can be circumvented. At the receiver, INI is created by FFT process of the received signal. In the case of NSN receiver, for example, NCP -sized CP is first removed from the received composite signal followed by N -point FFT process to recover NSN subcarriers as shown in Figure 3.6(a). Note that, while FFT window at the NSN receiver captures N -sized NSN symbol from the composite signal, it also captures an incomplete WSN symbol which, upon FFT process, produces distorted WSN subcarriers whose side lobes cause an extra INI to the NSN subcarriers. Similar problem exists at the WSN receiver shown in Figure 3.6(b). In this case, the INI pattern shown in Figure 3.5 is completely destroyed. Analytically, INI power from vth subcarrier of WSN to the kth subcarrier of NSN is given by [6] INSN (k, v) =

ρ WSN (v) (k, v), N ×M for 0 ≤ k ≤ η1 N − 1 and {0 ≤ v ≤ η2 N − 1 : v/Q ∈ Z}, (3.9)

∗

Non-orthogonality due to CP exists even in single-numerology systems, but in that case, system retains its orthogonality after CP removal at the receiver.

Mixed numerology OFDM and interference issues

75

where

sin [(π/Q)(1 + (1 − Q)CPR )(v − k)]2 (k, v) = sin [(π/N )(v − k + η1 N )]2 sin [(π/Q)(1 + CPR )(v − k)]2 +(Q − 1) × , sin [(π/N )(v − k + η1 N )]2

ρ WSN is the WSN subcarrier power, and CPR = NCP /N = MCP /M is the CP ratio employed by the system and its typical value is ≈ 0.07 for 5G NR. The term v − k is referred to as spectral distance between the victim NSN subcarrier at k and the interfering WSN subcarrier at v. Note that, when Q = 1, i.e., the system comprises a single numerology, INSN reduces to zero, as expected. Likewise, the INI from INI power from kth NSN subcarrier to the vth subcarrier of WSN is given by [6] IWSN (k, v) =

ρ NSN (k) |ξ (k, v)|2 , N ×M for 0 ≤ k ≤ η1 N − 1 and {0 ≤ v ≤ η2 N − 1 : v/Q ∈ Z}, (3.10)

where ξ (k, v) =

sin [(π/Q)(k − v)] . sin [(π/N )(k − v − η1 N )]

Like in the previous case with (3.9), (3.10) also reduces to zero when Q = 1. An interesting observation can be observed from (3.10) for NSN subcarriers occupying indices k such that k/Q ∈ Z. In this particular case, both k and v are integer multiple of Q, which makes the numerator term in the expression of ξ (k, v) and consequently the IWSN (k, v) zero. In other words, NSN subcarriers whose subcarrier indices are integer multiple of Q do not cause any interference on WSN subcarriers. This special case is illustrated in Figure 3.7 for Q = 2, i.e., f1 = 15 kHz and f2 = 30. Such a peculiar property of INI can be useful from scheduling perspective. For instance, subcarriers of NSN user that, for some reasons, require higher transmission power can be scheduled, through an interleaved subcarrier mapping technique, to occupy these specific indices so that they do not cause extra INI to the adjacent WSN subcarriers. Generic INI patterns for both NSN and WSN are visually illustrated in Figure 3.8. Unsurprisingly, edge subcarriers of each numerology are experiencing higher INI compared to the middle ones. This is because of the fact that, side lobes, which are the ones interfering with the adjacent numerology, are stronger at the edges and their strength tends to abate as one moves away from the edges. Another intriguing property that can be observed from Figure 3.8 is the oscillatory nature of the INI experienced by NSN subcarriers. This behavior is heuristically attributed to the discontinuities created at the boundaries of the Q-concatenated WSN symbols within TLCM duration, and as it will be shown in the next section, the pattern of the oscillation varies with Q.

76

Flexible and cognitive radio access technologies for 5G and beyond 0.03

NSN WSN

0.025

INI power (linear)

0.02

0.015

0.01 0 44

46

48

50

0.005

0 0

20

40

60

80

100

120

Subcarrier index

Figure 3.7 INI distribution when NSN user utilizes subcarriers whose indices are integer multiple of Q only (in this case Q = 2)

3.4 Factors affecting INI Although we have mentioned that the main source of INI is the lack of orthogonality between subcarriers belonging to different numerologies, there are some other factors that determine the level of severity of the INI in the system. From the INI expressions given in the previous section, factors such as subcarrier spacing ratio Q and subcarrier power ρ of the multiplexed numerologies can be straightforwardly associated with the intensity of INI suffered by each numerology. In this section, detailed analysis of these factors is presented.

3.4.1 Subcarrier spacing ratio, Q One important insight that can be drawn from the analytical expressions presented in the previous section is that INI is a function of the ratio Q of the subcarrier spacing rather than the actual subcarrier spacing of the multiplexed numerologies. That is, multi-numerology system with f1 = 15 kHz and f2 = 30 kHz suffers exactly the

Mixed numerology OFDM and interference issues

77

0 NSN analytic

0

NSN simulation

−5 −5

WSN analytic

−10

WSN simulated

−15

INI (dB)

−10

−20 50

60

70

−15

−20

−25 20

40

60

80

100

120

Subcarrier index

Figure 3.8 INI power experience by each subcarrier in each subband for N = 128, Q = 2, and CPR = 1/16 same amount of INI as another system with f1 = 60 kHz and f2 = 120 kHz as they both have Q = 2. Additionally, it can be deduced from INI expressions (3.9) and (3.10) that INI power inflicted on each subcarrier increases with the increase in Q. Increase in Q can be interpreted as decreasing M for a given value of N that leads to the minimization of the product N × M in (3.9) and (3.10), resulting in higher INI for both numerologies. Therefore, it can be concluded that multi-numerology system with low Q suffers less INI. This conclusion establishes an important fact concerning scheduling of numerologies in frequency domain. In order to minimize system INI, numerologies that constitute a minimum Q should be scheduled adjacent to each other. This discussion is summarized in Figure 3.9 where INI experienced by each subcarrier of each numerology is observed for different values of Q. As mentioned in the previous section, the oscillatory pattern of INI on NSN is more pronounced in Figure 3.9, and it can be seen clearly that period of the oscillation is Q subcarriers.

3.4.2 Power offset Practically, users can have different power requirement depending on their channel conditions or use case. For instance, in order to preserve battery life of the device,

78

Flexible and cognitive radio access technologies for 5G and beyond 5

NSN analytic: Q = 2 NSN simulation: Q = 2 WSN analytic: Q = 2 WSN simulation: Q = 2 NSN analytic: Q = 4 NSN simulation: Q = 4 WSN analytic: Q = 4 WSN simulation: Q = 4 NSN analytic: Q = 8 NSN simulation: Q = 8 WSN analytic: Q = 8 WSN simulation: Q = 8

0

INI (dB)

–5

–10

–15

–20

–25 20

40

60

80

100

120

Subcarrier index

Figure 3.9 INI pattern for NSN-WSN pair with different values of Q

mMTC applications operate at lower power level compared to other 5G use cases. Diverse power levels among users within the same frame create power offsets. In the case of a conventional single numerology, where no INI exists, power offset does not cause any performance degradation within the frame. Contrarily, power offset elevates the intensity of INI experienced by user operating at low power within a mixed numerology frame. Expressions (3.9) and (3.10) only give insight on the effect of power of the aggressor numerology on the INI experienced by a victim numerology. They clearly show that INI suffered by a victim numerology is a function of the subcarrier power, ρ, of the aggressor numerology. However, the effect of power difference between multiplexed numerologies might not be directly inferable from these expressions. In order to do so, let us observe the issue from signal-to-interference ratio (SIR) perspective. Assuming INI to be the only source of interference in the system, SIRs of the kth NSN subcarrier and vth WSN subcarrier are given by SIRNSN (k) =

ρ NSN × N × M , 2 N −1 2 ρ WSN | ηv=0,v/Q∈ Z (k, v)|

(3.11)

Mixed numerology OFDM and interference issues

79

and SIRWSN (v) =

ρ WSN × N × M , 1 N −1 ρ NSN | ηk=0 ξ (k, v)|2

(3.12)

respectively. It is obvious from these SIR expressions that any power offset, i.e., when ρ NSN > ρ WSN or ρ WSN > ρ NSN , favors the numerology with high power while degrading performance of the numerology with relatively lower power as shown in Figure 3.10. This observation is quite expectable as the numerology with higher power has stronger side lobes that cause more severe interference to the adjacent numerology. The fair case can be observed when ρ NSN = ρ WSN , where SIR performance of each numerology becomes independent of the subcarriers power. Overall average system SIR also varies with power offset as shown in Figure 3.10. Although the trend is the same, overall average SIR is higher when ρ NSN > ρ WSN compared to the case with ρ WSN > ρ NSN . This observation is attributed to the number of subcarriers of each numerology. For a given bandwidth, NSN has a larger number of subcarriers compared to WSN, making its contribution to the overall SIR greater than that of WSN. However, it is worth noting that this observation is subjected to change when η1 = η2 as assumed in the result of Figure 3.10. 32 30 28

Average SIR (dB)

26 24 22 20

NSN:

ρNSN > ρWSN

WSN:

ρNSN > ρWSN

Average: ρNSN > ρWSN

18

WSN:

ρNSN < ρWSN

NSN:

ρNSN < ρWSN

Average: ρNSN < ρWSN

16 14 12 0

1

2

3

4

5

6

7

Power offset (linear)

Figure 3.10 Average SIR of the mixed numerology system as a function of power offset

80

Flexible and cognitive radio access technologies for 5G and beyond Composite signal

Composite signal

HNSN

HNSN HWSN

HWSN

Base station

NSN user

WSN user

(a)

NSN user Base station (b)

WSN user

Figure 3.11 Mixed numerology signal transmission: (a) downlink and (b) uplink

3.4.3 Channel response The INI expressions given in Section 3.3 consider a downlink transmission. In the downlink, signals that belong to different numerologies are superposed in time domain to form a composite signal that is then broadcast to both users, as shown in Figure 3.11(a). In this case, both NSN and WSN signals contained in the composite signal experience the same channel as they arrive at the receiver of any of the two users. Upon equalization of the received composite signal at the receiver of a given user, channel effect is eliminated for both desired and undesired signals. Consequently, the INI expressions obtained in Section 3.3 are independent of the channel responses. The situation is, however, different when we consider an uplink transmission shown in Figure 3.11(b). Here, NSN and WSN signals pass through different channels before they superpose at the receiver (i.e., a base station (BS) in this case). Consequently, equalization process at the receiver of a desired numerology does not completely eliminate channel effect experienced by the signal of the undesired numerology. Therefore, the resulting INI in the uplink scenario is a function of the channel response as given in [4].

3.5 INI management INI, like any other form of interference, if not handled properly, can significantly degrade the performance of the system. To this end, several techniques have been proposed in the literature to overcome INI. In essence, it has been shown that some of the traditional approaches of handling interference are applicable to INI as well. Techniques such as insertion of GB between interfering numerologies, use of windowing or filtering, intelligent resource allocation, and scheduling have been shown to be effective means of minimizing severity of INI in mixed numerology systems. In this section, detailed discussion of some of these techniques is presented.

3.5.1 Restructuring INI through common CP In Section 3.3, we observed that INI is a well-structured form of interference with a well-defined pattern. For instance, it is shown in Figure 3.7 that there is a specific set of NSN subcarriers, whose subcarrier indices are integer multiple of Q, that exhibit some

Mixed numerology OFDM and interference issues

81

kind of unidirectional orthogonality with WSN subcarriers. That is, these particular NSN subcarriers cause no interference to all WSN subcarriers but they, themselves, are victims of INI from WSN. It was then highlighted that such kind of INI pattern can be leveraged to mitigate INI by, for example, performing an INI-aware scheduling. Building on top of this observation, a novel multi-numerology symbols alignment technique that extends the degree of orthogonality in mixed numerology system was proposed in [6]. The proposed technique, referred to as common CP, is based on the multi-symbol encapsulated OFDM (MSE-OFDM) concept originally proposed in [7]. MSE employs a different way of inserting CP to the OFDM symbols. With this approach, a number of OFDM symbols are grouped† together as a frame that is then protected using one single CP. Essentially, this approach was originally proposed to lower peak-to-average power ratio (PAPR) of an OFDM signal, increase its robustness against timing synchronization error, and reduce efficiency loss due to the redundancy introduced by CPs [7,8]. Later, common CP symbol alignment technique was derived from this concept, specifically for mixed numerology system. In the mixed numerology system, common CP technique is normally applied on WSN symbols. In this case, instead of inserting CP to each of the Q-concatenated WSN symbols, the symbols are first concatenated to form a long signal of length Q × M ( = N ) samples whereupon a CP of length Q × MCP ( = NCP ) is appended, as shown in Figure 3.12. Common CP for mixed numerologies was first studied in [9], the main motivation being to create an OFDM symbol structure which is robust against both delay and Doppler spread effect in the case of a doubly dispersive channel. In the 5G standards, CP size decreases with the increase in subcarrier spacing (see Table 1.7 in Chapter 1). In the case of a doubly dispersive channel, there is no much flexibility of having both larger subcarrier spacing and larger CP duration to simultaneously overcome

q=0

q=1

q = Q −1

NCP

TLCM

Figure 3.12 Application of common CP on WSN symbols † In the case of time-varying channel, a number of OFDM symbols that can be grouped strictly depend on the coherence time of the channel. This is in order to facilitate one-tap frequency domain equalizer (FDE) at the receiver.

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Flexible and cognitive radio access technologies for 5G and beyond Rx Equalization with common CP CP removal

y

(Q × M)point FFT

Equalization

(Q × M)point IFFT yEq

Detection

FFT{yEq(qM + 1 : (q + 1)M)}|M-point

Figure 3.13 A modified OFDM receiver for common CP configuration

both inter-carrier interference (ICI) and inter-symbol interference (ISI). Common CP provides a solution to such a dilemma without compromising system performance and complexity. At this juncture, it is worth mentioning that in order to facilitate proper equalization and detection process with common CP, some modifications on the traditional OFDM receiver structure are mandatory. Specifically, extra FFT and IFFT blocks are required as shown in Figure 3.13. Equalization is performed over the whole block of Q concatenated symbols. The equalized frequency domain signal is then transformed back to time domain where each of the Q symbols is decoded separately through a conventional M -point FFT process. The receiver processing with common CP is summarized in Figure 3.13 where y denotes a received signal that consists of Q WSN symbols after CP removal, and yEq is the same signal after equalization process. More detail and analysis of the common CP receiver can be found in [7,9].

3.5.1.1 INI analysis with common CP As mentioned earlier, common CP is only used on WSN symbols, whereas a conventional CP configuration is used for each NSN symbol. Consequently, xWSN given in (3.6) is modified to xWSN =

Q−1

xwdq (m − NCP − qM ),

(3.13)

q=0

where the expression for yNSN given in (3.3) is still valid, and the consequent downlink composite signal is as expressed by (3.8). WSN and NSN symbol alignment over TLCM duration is shown in Figure 3.14. Note that, unlike the conventional symbol alignment shown in Figure 3.4 with common CP configuration, CPs of both NSN and WSN are perfectly aligned such that no data portion of one numerology interferes with a CP portion of the other. Referring to our discussion in Section 3.3, it is apparent that upon CP removal at the receiver of either numerology, the subcarriers of the multiplexed numerologies regain the structure shown in Figure 3.5. In other words, contrary to the conventional CP case,

Mixed numerology OFDM and interference issues NCP

83

N

q=0

q=1

q=Q−1

TLCM

Figure 3.14 Multi-numerology symbols alignment with common CP

FFT window at WSN receiver

FFT window at NSN receiver Received composite signal NSN WSN (a)

(b)

Figure 3.15 Illustration of FFT windows at (a) NSN and (b) WSN receivers with common CP for Q = 2 non-orthogonality due to CP insertion can be completely eliminated at the receiver when common CP configuration is adopted. Observe that FFT window designated to capture an N -sized NSN symbol at the NSN receiver also captures all Q WSN symbols perfectly, as shown in Figure 3.15(a). Consequently, FFT process to recover NSN subcarriers does not distort WSN subcarriers. Therefore, no extra INI is imposed on NSN due to the FFT process as in the case with conventional CP. Hence, the INI experienced by NSN is expected to assume the pattern observed in Figure 3.5, which is mathematically given by INSN (k, v) =

ρ WSN (v) |(k, v)|2 , M2

(3.14)

where (k, v) =

sin [(π/Q)(v − k)] . sin [(π/N )(v − k + η1 N )]

Note that when k bears a value which is an integer multiple of Q, i.e., k/Q ∈ Z, the term (k, v) in (3.14) reduces to zero, which implies that all NSN subcarriers

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occupying the indices which are integer multiple of Q experience zero INI from WSN subcarriers. On the other hand, FFT window at the WSN receiver captures an incomplete NSN symbol from the composite signal (see Figure 3.15(b)) which, upon FFT process, will cause INI to the WSN subcarriers as in the conventional CP case. Basically, there is no significant difference between FFT processes at the WSN receiver with conventional or common CP; hence, INI expression for this case is similar to the one derived for the conventional CP (i.e., (3.10)), as shown in [6]. Combining the insights drawn from (3.10) and (3.14), it is clear that, with common CP configuration, NSN subcarriers occupying indices which are integer multiple of Q neither cause nor receive any interference from WSN. In other words, this particular set of NSN subcarriers becomes fully orthogonal with all WSN subcarriers. This claim is emphasized by Figure 3.16 where it is shown that if NSN utilizes only those subcarrier indices which are integer multiple of Q, the whole system experience zero INI. This orthogonality is, however, disguised when all NSN subcarriers are used.

0.03

NSN WSN

INI power (linear)

0.025

0.02

0.015

0.01 0 44

46

48

50

0.005

0 0

20

40

60

80

100

120

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Figure 3.16 INI distribution when NSN utilizes subcarrier indices that are integer multiple of Q (Q = 2 in this case) and WSN utilizes common CP

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A complete INI pattern for both NSN and WSN with common CP is shown in Figure 3.17 for different values of Q. Note that, based on our orthogonality discussion earlier, the INI experienced WSN as shown in Figure 3.17 is only due to the NSN subcarriers whose indices are not integer multiple of Q. In the NSN subband, it is clear that the INI becomes zero periodically (i.e., after every Q subcarriers). Similar observation is reported in [5] for multi-numerology system utilizing universal filtered multicarrier (UFMC) waveform, indicating that choice of waveform is one of the key factors that determine the level and structure of the INI experienced by the system. The level of orthogonality exhibited with the aid of common CP configuration can be of great advantage in a number of practical aspects. For instance, pilot subcarriers for channel estimation in NSN subband can be distributed on those subcarriers with zero INI to enhance estimation accuracy. Also, subcarriers of a reliability-sensitive user can be scheduled to occupy NSN subcarriers with zero INI in order to reduce their error probability. In the next subsection, an INI-aware GB allocation scheme that exploits this particular characteristic of INI is discussed.

1

NSN analytic: Q = 2 NSN simulation: Q = 2 WSN analytic: Q = 2 WSN simulation: Q = 2 NSN analytic: Q = 4 NSN simulation: Q = 4 WSN analytic: Q = 4 WSN simulation: Q = 4 NSN analytic: Q = 8 NSN simulation: Q = 8 WSN analytic: Q = 8 WSN simulation: Q = 8

0.9 0.3

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INI power (linear)

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0.4

50

55

60

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20

40

60 Subcarrier index

80

100

120

Figure 3.17 INI distribution for a mixed numerology system with common CP scenario for different values of Q

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Flexible and cognitive radio access technologies for 5G and beyond 0.6

0.5

Individual CP: NSN analytic Individual CP: NSN simulation Individual CP: WSN analytic Individual CP: WSN simulation Individual CP: NSN simulation Individual CP: NSN analytic Individual CP: WSN simulation Individual CP: WSN analytic

0.1 0.08

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75

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Figure 3.18 INI pattern comparison for conventional and common CP configurations for Q = 2 Figure 3.18 compares the INI power experienced by each subcarrier of NSN and WSN for both conventional and common CP configurations. One critical thing to be observed from Figure 3.18 is that the NSN subcarriers occupying indices which are not integer multiple of Q experience higher INI with common CP than with conventional CP. Therefore, the fact that common CP has an advantage of having a number of INI-free NSN subcarriers comes at a cost of an increased INI on the other NSN subcarriers. In essence, for a given Q, total INI in the NSN subband is the same for both CP configurations. However, it can be regarded that common CP localizes this INI to only some of the subcarriers. Again this property can be exploited to reduce the complexity of the INI cancelation algorithms. Subcarriers that cause or receive zero INI do not have to be considered in the cancelation process.

3.5.2 INI-aware scheduling Based on the analysis presented so far, it is vivid that scheduling practice that considers the observed INI characteristics can efficiently minimize the impact of INI on the system without resorting to the complex or spectrally inefficient approaches. Throughout

Mixed numerology OFDM and interference issues

Numerology-2

Numerology-3

Time

Numerology-1

87

Frequency

Increasing ∆ f

Figure 3.19 Illustration of mixed numerology scheduling based on their fs

our discussion earlier, we have pointed out some potential INI features that can be leveraged in devising INI-aware scheduling algorithms. Some basic INI-aware scheduling practices are summarized in the following: ●

●

One of the pivotal observations regarding numerology scheduling that guarantees minimum INI is scheduling numerologies that constitute minimum Q (=f2 /f1 ) adjacent to each other. In that case, users to be scheduled in the same frame should first be sorted in ascending/descending order based on their SCSs such that users utilizing the same numerology are grouped together and each succeeding group constitutes minimum possible Q with the preceding one. An illustration is given in Figure 3.19 for a frame supporting three different numerologies. Users within a given group (i.e., group of users utilizing the same numerology) should be scheduled based on their reliability constraints [10]. Particularly, reliability-sensitive users should not be scheduled at the edge where INI is dominant. Furthermore, the decision on the edge user for each group should be done jointly such that edge users in the adjacent numerologies constitute minimum power offset possible. Minimum or zero power offset facilitates a fair SIR performance for both users as discussed in Section 3.4.2. Such scheduling practice is illustrated in Figure 3.20.

The study in [11] exploits these INI-aware scheduling tips along with guard optimization techniques to minimize the impact of INI while ensuring minimum loss in spectral efficiency. The guard in time (i.e., guard duration (GD)) and guard in frequency (i.e., GB) are jointly optimized regarding power levels and SIR requirements of the edge users. Performance analysis of such scheduling practice revealed that, compared to the random scheduling, the amount of GB and GD are reduced by 27% and 10%, respectively, for a mixed numerology system utilizing OFDM numerologies with windowing technique.

Flexible and cognitive radio access technologies for 5G and beyond

Power

88

UE-2

UE-7 UE-10

UE-3

UE-1

UE-9

UE-5

UE-8

UE-6

UE-4

Users with numerology-1

Users with numerology-2 Frequency

(a)

Power

Rescheduled to minimize power offset around the edge UE-2 UE-1

UE-7 UE-10

UE-3

UE-9

UE-5 UE-4

UE-6

Users with numerology-1

UE-8

Users with numerology-2 Frequency

(b)

Figure 3.20 Illustration of the edge user scheduling considering power offset: (a) random scheduling and (b) power-offset aware scheduling

3.5.3 INI-aware guard band allocation Insertion of the GB between adjacent subbands utilizing different numerologies is a conventional and straightforward approach of minimizing effect of INI. In general, every user leaves a number of edge subcarriers unused in order to allow its side lobes to attenuate and thus meet the spectrum mask requirements. Here it is pertinent to mention that edge subcarriers of each numerology are main victims of INI from adjacent subbands and, at the same time, the main aggressor of it to the nearby bands due to their strong side lobes. Therefore, by leaving these subcarriers unused, a user improves its own overall bit error rate (BER) performance as well as protects adjacent users from interference by minimizing power of its side lobes leaking into their bands. Size of the required GB is usually determined by a number of factors such as users’ desired SIR performance, power offset between the interfering subbands, and filter type in the case of filtered OFDM. As mentioned earlier, most of the classical techniques such as filtering and windowing can be used to handle INI. However, in some cases, these techniques cannot

Mixed numerology OFDM and interference issues

89

fully control the interference such that a nontrivial amount of GB might still be required. For instance, it is shown in [12] that in order to have a sufficiently good link performance that supports a high-order modulation in a mixed numerology scenario, at least three physical resource blocks might be required along with transmitter and receiver windowing operations. Therefore, in most cases, usage of the GB is strictly inevitable [13]. Figure 3.21 illustrates a traditional way of implementing GB. G1 and G2 are GBs on NSN and WSN subbands, respectively, and G is the total GB in the system (i.e., G = G1 + G2 ). Figure 3.22 visually shows an effect of GB on INI for a simple case of two numerologies with Q = 2 and no power offset between them. In such a simple scenario, allocating only a few subcarriers as a GB might be enough to bring INI into a tolerable level. However, some use cases such as eMBB-based applications require high modulation order to achieve their targeted high data rates, which in turn necessitate a substantial amount of GB to ensure a reliable transmission against interference. In such cases, the conventional way of allocating GB becomes unattractive as it severely compromises the spectrum utilization efficiency. If B is the systems’ bandwidth, the total utilized bandwidth (UBW) is obtained as B − G and the spectrum utilization efficiency, E, is given as E=

UBW G =1− . B B

(3.15)

The INI pattern described in Sections 3.3 and 3.5.1 can be leveraged to overcome such plethoric wastage of frequency resource. We have seen that, with both conventional and common CP configurations, NSN subcarriers occupying subcarrier indices which are multiple integer of Q cause zero INI to the WSN subcarriers. This suggests that all the subcarrier indices within the GB region which obey the aforementioned condition can be invoked to carry extra NSN subcarriers without elevating the level of INI experienced by the adjacent WSN subcarriers [14]. Consequently, a significant portion of the allocated GB can be harnessed for data transmission. This approach is referred to as INI-aware GB allocation, and it is illustrated in Figure 3.23 for the

Q×∆f

Power

∆f

∆G Frequency

Figure 3.21 Illustration of the conventional GB allocation between adjacent subbands

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Flexible and cognitive radio access technologies for 5G and beyond 0.5

∆G = 0

0.06

∆G1 = 2 × ∆ f1 ∆G2 = 1 × ∆ f2

0.04

∆G1 = 5 × ∆ f1 ∆G2 = 3 × ∆ f2

0.4

0.02

INI power

0.3

0 50

60

70

80

0.2

0.1

0 0

20

40

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80

100

120

Subcarrier index

Figure 3.22 INI mitigation using GB Q×Δf

Power

Δf

ΔG

Frequency

Figure 3.23 INI-aware guardband allocation for Q = 2 case of Q = 2. Quantitatively, up to G/Q Hz of the allocated GB can be recovered, making the spectrum utilization efficiency, E , of this approach be E =

UBW + G/Q G =E+ . B B×Q

(3.16)

Mixed numerology OFDM and interference issues

91

Observe from (3.16) that the improvement on the spectrum efficiency depends on the two main factors: the amount of GB originally employed by the system and the ratio Q of the multiplexed numerologies. If the system’s GB is small, only a minimal improvement of the efficiency can be achieved, which can be considered insignificant. But in the case of larger GB, this approach proves to be instrumental in saving the frequency resource allocated as GB. It is also crucial to note that the amount of GB that can be recovered using this approach increases with the decrease in Q, i.e., the maximum performance is attained when multiplexed numerologies that constitute the minimum Q are scheduled adjacent to each other. For instance, up to 50% of the GB is recovered when Q = 2. However, it should be noted that in the conventional CP configuration, although the NSN data carriers inserted into the GB region do not cause any interference to the adjacent WSN numerology, they themselves are prone to high INI from WSN subcarriers (refer to the concept of “unidirectional orthogonality” with conventional CP as illustrated in Figure 3.7). In order to ensure a reliable communication and not to degrade overall BER performance of the NSN user, a low-order modulation is recommended for the symbols transmitted by NSN subcarriers within the GB region. Low-order modulation formats such as binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) are robust against a low signal-to-interferenceplus-noise ratio (SINR) communication links. Note that, in this case, modulated symbols carried by subcarriers outside the GB region can still utilize higher order modulation formats as desired. Such limitations are not applicable in the case of common CP configuration since with common CP the subcarriers utilized within GB are full orthogonal with the WSN subcarriers (see Figure 3.16).

3.6 Asynchronicity in the mixed numerology frame All our discussion so far has been based on the mixed numerology system that assumes perfect synchronization within the frame by considering symbol alignment over TLCM duration. However, in practical systems, such perfectly synchronized frames might not always be guaranteed. For instance, due to the hardware impairments in the real systems, some amount of carrier frequency offset, time offset, and phase noise will always be present. Most importantly, the considered perfect symbol alignment over the TLCM duration will not be possible when different overheads (such as filter tails or extra guard intervals for windowing purpose) are employed by multiplexed numerologies. In such cases the frame turns out to be asynchronous in both frequency and time domains. Therefore, in additional to INI, other forms of interference such as ICI and ISI should be taken into account for a more realistic analysis. ISI can be primarily due to insufficient CP. As briefly mentioned earlier, the CP ratio is fixed for 5G NR (except for the special case of 60 kHz numerology) such that larger f implies smaller CP duration which in some cases might not be enough to contain the channel delay spread. In general, if we consider system imperfections, the total interference power on each subcarrier of either numerology is modeled as the summation ICI, ISI, INI, and

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Flexible and cognitive radio access technologies for 5G and beyond

noise [5]. Analysis shows that, unlike INI whose effect is more severe on the edge subcarriers, the magnitude of ICI and ISI is approximately the same for all subcarriers in a given numerology. It is further deduced that even the usage of the filtered waveform such as UFMC is not effective enough in minimizing INI on edge subcarriers [5]. This observation, to a large extent, supports the fact that INI management techniques should be utilized in conjunction with the INI-aware scheduling mechanisms.

3.7 Mixed numerology in single-carrier schemes Although the majority of the mixed numerology studies in the literature are based on the multicarrier waveforms, the study in [15] has stretched the concept of numerology to the single-carrier (SC) domain. In the conventional SC system utilizing a raised cosine (RC) pulse, the choice of roll-off factor (α) and truncation duration (ζ ) are usually made independent of each other and the selected values are kept fixed within a given block as shown in Figure 3.24(a). Authors have proposed an SC system where the values of the controllable parameters of the RC pulses are selected jointly and adaptively for each symbol within the block to minimize trade-offs between conflicting performance goal functions. The applicability of the mixed numerology concept in SC system is demonstrated for URLLC-based applications with low latency and high reliability as goal functions. URLLC service is arguably the most challenging service class of 5G due to its stringent and yet conflicting requirements. In SC, latency can be lowered by minimizing the span of the RC pulse (i.e., setting ζ to a small value). Apart from minimizing the latency, small ζ also ensures low PAPR of the SC signal due to the reduced number of symbols per pulse which can potentially improve system reliability. However, small ζ means an excessive truncation of the RC pulse which causes loss of its orthogonality property and, consequently, ISI between symbols within the block. On the other hand, RC pulses with higher α are more localized in time domain (i.e., have low out-of-band emission (OOBE) and thus low ISI within the block due to their reduced side lobes) making them more suitable for the reliability sensitive applications. In order to achieve both low latency and reliable transmission, an intelligent adaptation of α and ζ for each RC pulse within the block is studied. The pulses with low α but larger ζ (to ensure that orthogonality of the pulse is maintained and thus low ISI) are used for inner symbols. The value of α is gradually increased and that of ζ decreased toward the edges of the block as shown in Figure 3.24(b). High α and low ζ at the edges make the block more localized in time ensuring that low latency during transmission and low out-of-block emission are also achieved as an extra advantage. Note that RC pulses with high α have very low side lobes whose ISI due to low ζ is not significant. In this way, both reliability and latency can be guaranteed in SC systems.

3.8 Summary Mixed numerology frame structure was introduced in 5G standards as a step toward the desired ultimate radio system flexibility. However, the existence of INI problem

Mixed numerology OFDM and interference issues

93

α = [0]

0.08

Amplitude

0.06

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α = [0:1]

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0 Time (T)

(b)

Figure 3.24 Pulse shaping for traditional and proposed [15] SC system: (a) traditional SC block with fixed α and (b) proposed SC block with adaptive α

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has set some limitation on the harmonic coexistence of data carriers with different numerologies within the same frame. Throughout this chapter, an extensive analysis and in-depth discussion of the INI problem is presented regarding 5G standards and the potential common CP OFDM symbol structure for future wireless generations. Some important facts to be noted from the presented analysis and discussion are that although different numerologies have been generally considered to be non-orthogonal, there is, however, some degree of orthogonality between some of the NSN and WSN subcarriers which can be leveraged to improve system performance against INI. It is further stressed that this degree of orthogonality can be enhanced through intelligent waveform design, common CP being an example. Vision of the next generations of wireless technologies is geared toward an ultimate flexibility in every dimension. Achieving such a lofty target requires rethinking of the current INI-limited mixed numerology frame structure. In this chapter, it is briefly shown that the concept of the mixed numerology can be extended to other domains in which INI problem is either minimal or does not exist at all. Such extensions include time domain OFDM numerology multiplexing, SC scheme-based mixed numerology, as well as mixed waveform-based multi-numerology systems. Detailed explanation of such extensions is presented in the next chapter.

References [1] Ankaralı ZE, Peköz B, and Arslan H. Flexible Radio Access Beyond 5G: A Future Projection on Waveform, Numerology, and Frame Design Principles. IEEE Access. 2017;5:18295–18309. [2] KiheroAB, Solaija MSJ,YazarA, et al. Inter-Numerology InterferenceAnalysis for 5G and Beyond. In: IEEE GLOBECOM Workshops (GC Wkshps). Abu Dhabi, United Arab Emirates; Dec. 2018. p. 1–6. [3] Sahin ¸ A, and Arslan H. Multi-User Aware Frame Structure for OFDMA Based System. In: IEEE Vehicular Technology Conference (VTC Fall). Quebec, Canada; Sep. 2012. p. 1–5. [4] Zhang X, Zhang L, Xiao P, et al. Mixed Numerologies Interference Analysis and Inter-Numerology Interference Cancellation for Windowed OFDM Systems. IEEE Transactions on Vehicular Technology. 2018;67(8): 7047–7061. [5] Zhang L, Ijaz A, Xiao P, et al. Subband Filtered Multi-Carrier Systems for Multi-Service Wireless Communications. IEEE Transaction on Wireless Communication. 2017;16(3):1893–1907. [6] Kihero AB, Solaija MSJ, and Arslan H. Inter-Numerology Interference for Beyond 5G. IEEE Access. 2019;7:146512–146523. [7] Xianbin W, Yiyan W, and Chouinard JY. On the Comparison Between Conventional OFDM and MSE-OFDM Systems. In: IEEE Global Telecommunications Conference (GLOBECOM). vol. 1. San Francisco, CA; Dec. 2003. p. 1–5.

Mixed numerology OFDM and interference issues [8]

[9] [10]

[11]

[12]

[13]

[14]

[15]

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Chouinard JY, Xianbin W, and Yiyan W. MSE-OFDM: A New OFDM TransmissionTechnique With Improved System Performance. In: IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP). vol. 3. Philadelphia, PA; Mar. 2005. p. 1–4. Nemati M, and Arslan H. Low ICI Symbol Boundary Alignment for 5G Numerology Design. IEEE Access. 2018;6:2356–2366. Yazar A, and Arslan H. Reliability Enhancement in Multi-Numerology-Based 5G New Radio Using INI-Aware Scheduling. EURASIP Journal on Wireless Communications and Networking. 2019;2019(110):1–14. Demir AF, and Arslan H. Inter-Numerology Interference Management With Adaptive Guards: A Cross-Layer Approach. IEEE Access. 2020;8:30378– 30386. Levanen T, Pirskanen J, Pajukoski K, et al. Transparent Tx and Rx Waveform Processing for 5G New Radio Mobile Communications. IEEE Wireless Communication. 2019;26(1):128–136. Demir AF, and Arslan H. The Impact of Adaptive Guards for 5G and Beyond. In: IEEE 28th Annual International Symposium on Personal, Indoor, Mobile Radio Communications (PIMRC). Montreal, Canada; 2017. p. 1–5. Memisoglu E, Kihero AB, Basar E, et al. Guard Band Reduction for 5G and Beyond Multiple Numerologies. IEEE Communication Letter. 2019;24(3):644–647. Tusha A, Do˘gan S, and Arslan H. Single Carrier Transmission for URLLC With Adaptive Radio Resource Utilization. In: 15th International Wireless Communications & Mobile Computing Conference (IWCMC). Tangier, Morocco; Jun. 2019. p. 1–5.

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Part II

Flexible waveform and modulation options for beyond 5G

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Chapter 4

Flexibility through hybrid waveforms Berker Peköz1 , Selçuk Köse2 , and Hüseyin Arslan1,3

In search of a flexible waveform, this chapter starts by manipulating orthogonal frequency division multiplexing (OFDM) into being more flexible to further optimize time and frequency localization to facilitate a better coexistence of different, more advanced numerologies. Dissatisfied with the performance of using manipulated OFDM waveforms solely, the hybrid waveform concept in which a plurality of currently available waveforms are used within a uniting frame structure to better satisfy various needs simultaneously is introduced. Waveform multiplexing approaches for hybrid waveforms are presented. Emerging solutions regarding determining a number of active numerologies, selection of active numerologies and scheduling of users to and within numerologies as part of the system conditions are discussed.

4.1 Introduction Cellular communication systems up to 4G have used symmetric and fixed waveforms for uplink and downlink, regardless of the different conditions between the legacy user equipment (UE) and base station (BS). Aside from amc, there was no flexibility in the waveform. Flexibility in the waveform domain was first introduced to cellular with the release of 4G. In 4G, the downlink waveform transmitted by the plugged in, high-quality power amplifier (PA)-equipped BSs are chosen as OFDM, whereas the waveforms transmitted by the battery-operated comparatively lower-end UEs are chosen as discrete Fourier transform spread OFDM, of which advantages over each other were discussed in Chapter 2. 5G expanded this waveform flexibility by allowing the use of different parameterizations of the same mother waveform, OFDM. The current state of the art used in 5G is simple parameter manipulation (subcarrier spacing and cyclic prefix (CP) duration) of the single waveform. While this flexibility has allowed the expansion of support to many new exciting services and applications, it also resulted in the new inter-numerology interference (INI) problem, sacrificing a great deal of potential gains that can be achieved. This problem is covered greatly in Chapter 3.

1

Department of Electrical Engineering, University of South Florida, Tampa, USA Department of Electrical and Computer Engineering, University of Rochester, Rochester, USA 3 Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Turkey 2

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This chapter begins with a presentation of the first step beyond 5G. In Section 4.2, OFDM is manipulated into being more flexible to further optimize time and frequency localization to facilitate a better coexistence of different, more advanced numerologies. The efforts described therein are not only limited to efforts aimed at minimizing time and frequency guards, but also minimizing the power consumption and the information leakage from the waveform to achieve the emerging secrecy requirements described in Chapter 18. The section highlights ingenious attempts at utilizing and manipulating various resources to achieve these goals; however, these attempts are not presented to demonstrate what can be done in full, but to inspire the many new waveforms that can be built between now and 6G standardization. We make a note of the fact that the next step toward flexibility would be to seek a more flexible mother waveform than OFDM. While manipulations of OFDM are demonstrated in Chapter 3 and are continued in this chapter, these manipulations do not address all the factors that are discussed in Chapter 1. There is an apparent but heretofore unresolved need for a plurality of waveforms of which adjustable parameter spans beyond subcarrier spacing. While orthogonal time frequency and space [1], generalized OFDM [2], circularly pulse-shaped orthogonal frequency division multiplexing (CPS-OFDM) [3], polynomial-cancellation-coded orthogonal frequency division multiplexing (PCC-OFDM) [4] and filter bank multicarrier (FBMC) [5] can be considered primitive alternatives for this task, the community must seek and study new waveforms from the flexibility perspective. That being said, a waveform flexibility metric has not been defined to date to quantify the success of any effort that may come through. The high risk associated with this potentially highly rewarding pursuit is not suitable for the vibrant community aiming shorter term outcomes. A fruitful route that yields practical output until the arrival of the long-awaited flexible waveform is the investigation of the hybrid waveform concept in which a plurality of currently available waveforms are used within a uniting frame structure to better satisfy various needs simultaneously. In Section 4.3, waveform multiplexing approaches for hybrid waveforms are presented. An obvious but efficient way of multiplexing OFDM numerologies for narrowband systems is demonstrated. Considering the strengths and weaknesses of OFDM and its alternatives explained in Chapter 2, whether it is more beneficial to multiplex OFDM numerologies or numerologies of multiple waveforms to satisfy as much of the requirements described in Chapter 1 as possible considering the INI impact described in Chapter 3 is discussed. The “numerologybook” resulting from the combination of flexible structures presented herein in combination with various numerologies of various waveforms having different parameters spans a rather wide search space to assign users from. Given the massive number of devices, choosing a numerology from the numerologybook for each device itself becomes a computational burden [6]. Furthermore, as demonstrated in Chapter 3, each new numerology introduced to the system either degrades the other numerologies by interfering with them or, if this interference is avoided, constraints their resources. These issues have resulted in the emergence of a leading concept called numerology-based scheduling. In Section 4.4, these issues and emerging solutions regarding determining a number of active numerologies, selection

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of active numerologies and scheduling of users to and within numerologies as part of the system conditions are discussed before the chapter is concluded.

4.2 Improved OFDM-based flexible structures for beyond 5G applications While many waveforms are proposed for future generations, history discussed in Chapter 2 concludes that OFDM remains the standard. However, OFDM experiences multiple issues, the most critical ones being the high peak-to-average power ratio (PAPR) and the out-of-band emission (OOBE) characteristics of the waveform, severely limiting applications [7]. One other issue is the inherent insecurity of the waveform due to its autocorrelation characteristics because of CP. In this section, methods that remedy the basic OFDM structure from these shortcomings without changing the waveform to create a low PAPR, spectrally localized and secure waveform are discussed.

4.2.1 Spectrally localized OFDM OFDM has historically been praised for its granular spectrum access properties, but having the worst possible OOBE has been a challenge in this context, limiting spectral efficiency (SE) and potential use [8]. PAPR has also imposed stringent requirements on the hardware quality, pushing modem prices up and limiting communication range and battery life [9]. These long-standing classical issues have not lost, but on the contrary, gained importance as future communication system requirements emerged [10]. There are a plurality of other new waveforms covered in Chapter 2, most of which aim to solve mostly the high OOBE by making changes to the waveform processing. There are, however, other algorithms that can be embedded in the conventional CPOFDM structure itself. In this subsection, techniques that fall into the latter are discussed. Earlier stages of these techniques involved either allocating resources to facilitate these methods and containing the energy for these methods within these resources or performing complicated operations at the receiver to remove the effects of these methods to an extent, usually at the cost of reception performance degradation. The next-generation algorithm family involves a special transcription technique commonly referred to as “alignment,” which consists of a signal that covers the whole spectrotemporal resource grid at the transmitter, providing better performance as all terms are modified, and aligns to a specified region at the receiver after a comparably simpler operation, such as passing the received signal through a filter, or as the technique evolves, after the signal passes through the channel itself. Once the signal aligns to the predefined spectrotemporal resources, these elements are discarded and the reception performances at the information-carrying elements remain unaffected.

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The presented methods reduce PAPR and OOBE through flexibilities of the frame structure by inserting time-domain signal in Section 4.2.1.1, flexibilities of modulation by manipulating IQ data symbols as needed to comply to a spectral mask in Section 4.2.1.2 and flexibilities in subcarrier mapping in Section 4.2.1.3. However, the flexible waveform presented in Section 4.2.1.1 requires changes to frame structures to separate the injected signal from the information carrier; Section 4.2.1.2 resulted in, although minimal, degradation in detection performance; and Section 4.2.1.3 required the transmission of the manipulation parameters to the receiver, resulting in control overhead. The findings in these evolutionary steps have shed light to the hybrid waveform, OFDM with alignment signals, signals that are injected to resources in conventional frame structures in such a way that they are disposed of upon reception. In Section 4.2.1.4, techniques of this family are discussed. Other techniques that are coupled with a special type of modulation [11] are also reviewed in Chapter 6, describing the modulation.

4.2.1.1 Adaptive symbol transitioned OFDM Through precoding, the transitions between OFDM symbols can be adjusted to create more spectrally localized OFDM frames. A method to realize this is commonly referred to as adaptive symbol transition (AST)-OFDM [12]. Visualized in Figure 4.1, this algorithm results in a temporal extension similar to windowed-OFDM (W-OFDM) wherein a is the introduced transition signal from the mth cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) symbol y(m) to m + 1th CP-OFDM symbol y(m+1) . However, instead of overlapping and adding weighted extensions of preceding and succeeding symbols as done in W-OFDM, this algorithm calculates the interference caused by the opportunist user to the victim licensed user (LU) conveying information in an adjacent band and adapts the transition signal by defining this interference as a linear least squares problem with a quadratic inequality constraint, wherein the constraint is the signal envelope, and optimizing the transition signal to minimize this interference. The scheme is investigated for potential implementation in cognitive radio applications, where the user aims to utilize the spectrum that is not used by the LU [13]. For example, if the LU is to employ the spectrum between the marked lines in Figure 4.2, the AST algorithm is seen to greatly improve the spectral localization of the opportunist signal and requires only four guard subcarriers on each side of the LU band if the maximum allowed normalized interference is −45 dB adjusted to the in-band energy. Although conventional CP-OFDM and raised cosine (RC) W-OFDM are

y(m)

y(m)

y(m+1)

a

y(m+1)

Figure 4.1 Temporal representation of input to the AST-OFDM algorithm and the resulting output

Flexibility through hybrid waveforms

103

0

Normalized PSD (dB)

−10

LU band

−20 −30 −40 −50 Conventional RC windowed AST

−60 −70 −128

−96

−64

−32

0

32

64

96

127

Normalized frequency (subcarrier index)

Figure 4.2 PSDs comparison of conventional CP-OFDM, RC windowed OFDM and AST-OFDM shown to utilize the same number of guard subcarriers for fair OOBE comparison, these waveforms would actually require a much higher number of guard subcarriers not to disturb the LU. Note that the information symbols, hence the detection performance, remain intact; however, there is a slight throughput degradation due to the temporal resources allocated for the AST signal, depending on its length.

4.2.1.2 Precoded OFDM Besides adjusting OFDM symbol transitions, the single-carrier (SC) data symbols multiplexed in subcarriers can be precoded to localize OFDM signals. Unlike adapting symbol transitions, such an approach does not require changes to the frame structure and is more compatible with legacy devices. However, manipulating symbols by precoding would obviously degrade the reception performance. Balancing the amount of performance degradation and spectral localization becomes a critical issue. A feasible solution balancing both would be to precoding the symbols just enough to satisfy the transmission mask [14]. As can be seen in Figure 4.3, this scheme precodes the data symbols instead of adding redundancies to the frame structure, such that the precoded data symbols comply to a mask imposed by the standard. This algorithm formulates the precoded data symbols as the solution of the quadratic programming problem minimizing the Euclidean distance between precoded and actual data symbols while satisfying the spectral mask linear inequality constraint. This formulation keeps the changes to the data symbols as little as possible while satisfying the spectral mask requirements. The OOBE reduction performance of this precoder is demonstrated in Figure 4.4. The precoder described in [15] can be seen to have better OOBE reduction performance, but given the mask requirements which are perfectly satisfied by [15], this unnecessary overkill is ought to result in performance degradations as no lunch can

104

Flexible and cognitive radio access technologies for 5G and beyond Transmitter

TX data

Channel coding and interleaving

Mod.

S/P

Mask compliant precoder

IFFT

P/S

Add CP

RF front-end Channel +

Noise Receiver

RX data

Channel decode and deinterleave

Demode

P/S

FDE

FFT

S/P

Remove CP

RF front-end

Figure 4.3 Block diagram of CP-OFDM transmitter/receiver pair having OOBE compliant to a mask imposed by the standard through precoding 0

Plain OFDM Tom’s precoder Relaxed mask

−10 Power spectrum (dBm/Hz)

−20 Power spectrum (dBm/Hz)

0

Plain OFDM Tom’s precoder Beek’s precoder 802.22 FCC mask

−40 −60 −80

−20 −30 −40 −50 −60

−100 −70 −120 −15

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0 5 −5 Frequency (MHz)

Power spectrum under a strict mask

10

15

−80 −15

(b)

−10

−5 0 5 Frequency (MHz)

10

15

Power spectrum under a relaxed mask

Figure 4.4 PSDs comparison of [14,15] under (a) 802.22 FCC mask and (b) relaxed mask be free. Before this degradation is revealed, another practical issue can be brought to attention. The 802.22 Federal Communications Commission (FCC) mask, in reality, is impractical as no other transceiver can actually utilize the adjacent band designated with the intermediate step response. If the OOBE requirements for this interval are relaxed as seen in Figure 4.4(b), this actually allows energy to better localize within the guard band and further reduces the OOBE in the adjacent channel, as similarly done in [16]. The bit error rate (BER) performance of Tom’s precoder [14] is compared to that of van de Beek’s [15] precoder and conventional OFDM in Figure 4.5. The performance degradation due to the excessive precoding performed by [15] becomes obvious as that scheme experiences an error floor, whereas Tom’s precoder causes little reception performance degradation in additive white Gaussian noise (AWGN) channels without experiencing any error floor and experiences an error floor only for higher order modulations in fading channels.

Flexibility through hybrid waveforms 100 10−11 10−2 10−3 10−4

10−2 10−3 10−4

10−5

10−5

10−6

10−6

0

2

4

6

8 10 12 14 16 Signal-to-noise ratio (dB)

18

Tom’s precoder 16-QAM fading Tom’s precoder 16-QAM Tom’s precoder adap. mod Tom’s precoder QPSK

10−1−1

Bit error rate

Bit error rate

100

Plain OFDM 16-QAM Plain OFDM QPSK Tom’s precoder QPSK Tom’s precoder 16-QAM Beek’s precoder QPSK Beek’s precoder 16-QAM Beek’s precoder adap. mod

20

(a)

105

Plain OFDM 16-QAM Plain OFDM QPSK Plain OFDM 16-QAM fading

0

2

4

6

8 10 12 14 16 Signal-to-noise ratio (dB)

18

20

(b)

Figure 4.5 (a) BER performance of Tom’s precoder [14] under a relaxed mask and van de Beek’s precoder [15] in AWGN channels, compared with conventional OFDM; (b) BER performance of Tom’s precoder in AWGN and multipath Rayleigh fading channels under a strict mask, compared with conventional OFDM Transmitter

TX data

Channel coding and interleaving

+

1,1

. . .

+

1,2

. . .

+

1,

. . .

+

. . .

+

0 1

. . .

Edge Subcarriers

. . .

S/P

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Edge Subcarriers

+

. . .

−1

+

2 −1

,1 . . .

Suppress sidelobes (Freq.)

,2

I F F T

. . . . . . . . .

Partial transmit sequences (Reduce PAPR)

. . . . . . . . .

Adaptive phase shifter (sidelobe supp. in time)

(

)

RF front-end

Delay

,

(

− 1)

subcarriers

+

:

=

, 0,

Channel

subcarriers

. . .

1

. . .

+

. . .

. . .

∈ , , otherwise

Receiver

RX data

Channel decode and deinterleave

Demode

P/S

FDE

FFT

Noise Revert phase shift, PTS, SLM

S /P

Remove CP

+

RF front-end

Figure 4.6 Block diagram of the three-stage joint OOBE and PAPR reduction using PTS technique

4.2.1.3 Partial transmit sequenced OFDM Aside from adapting symbol transitions and precoding data symbols themselves, the simple reordering of subcarriers and samples greatly affects the OOBE and PAPR characteristics of an OFDM signal. An example of such an algorithm is partial transmit sequences (PTS) [17]. As embodied in Figure 4.6, this algorithm first partitions a

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Flexible and cognitive radio access technologies for 5G and beyond

data vector X(m) carrying the SC data symbols of the mth OFDM symbol into V distinct and consecutive blocks Xv(m) , v ∈ Z+ ≤ V , where the instantaneous peak power is minimized by independently rotating the phase of each block. Then, the blocks at the two band edges, which are more critical to the OOBE, are partitioned to form L interleaved subblocks to allow shifting the phase of each successive edge subcarrier independently. Similar to the previous evolutions, the independent phase rotations to be applied to each subblock of the edge blocks such that OOBE to a victim’s band can be defined in a quadratically constrained quadratic program, which is efficiently solved by semidefinite relaxation [18]. The samples of the vth block of the mth CP-OFDM symbol yv(m) are obtained from the central blocks and the combination of phase rotated subblocks of the edge blocks and are fed to the second stage wherein PTS [19] are applied. This stage similarly shifts the phase of the samples of each block independently such that the crest ratio of the signal is minimized to obtain the time-domain samples z(m) to feed to the inter-symbol phase shifter block defining the third stage. The third stage of this algorithm deals with the transitions between consecutive OFDM symbols, as demonstrated in Figure 4.7. As discussed earlier, abrupt phase changes from one symbol to the other causes spikes in the OOBE, and the third stage circularly shifts the phase of the entire mth symbol to obtain μ(m) such that the phase jump between μ(m−1) and μ(m) is minimized. The effects of this multistage phase shifting on the OOBE can be seen in Figure 4.8. Although the second stage would also have a significant effect on the OOBE in practice after the signal passes through the nonlinear PA, this simulation does not consider this effect. Furthermore, the effect of number of blocks V and the number of edge subblocks L are investigated in Figure 4.9. Increasing numbers decreases the number of carriers per block/subblock, adding more independence to the system but in return increasing the overhead as more phase shift values need to be transmitted to the receiver. Lastly, the effect of the choice of V and L variables on the PAPR can be seen in Figure 4.10, along with a comparison to plain OFDM. It becomes obvious that the PAPR is independent of L as expected, and even the minimum V value of 2 results in a 1.5-dB decrease in PAPR compared to plain OFDM, and every time V is doubled, another 1.5-dB decrease is obtained.

−

CP 25

Improved phase transition

−

Figure 4.7 Phase of the OFDM symbol is circularly shifted to smoothen inter-symbol phase transitions in the third stage of [17]

Flexibility through hybrid waveforms Plain OFDM Stage 1 (Freq.) Stage 2 (PAPR) Stage 3 (Time)

0 –10 Normalized PSD (dB)

107

–20 –30 –40 –50

–60

–50

0

50

100

150

200

250

Normalized frequency (subcarrier index)

Figure 4.8 PSD of signals after each stage showing the individual impacts on sidelobe suppression for L = 8 and V = 2

Plain OFDM (V,L) = (2,8) (V,L) = (2,32) (V,L) = (4,8) (V,L) = (4,32)

0

Normalized PSD (dB)

–10 –20 –30 –40 –50

–60

–50

0 50 100 200 150 Normalized frequency (subcarrier index)

250

Figure 4.9 The effect of block sizes in [17] on the PSD

4.2.1.4 OFDM with alignment signals The techniques described in Sections 4.2.1.1–4.2.1.3 utilizing waveform flexibilities changed the frame structure, degraded reception performance and required control

108

Flexible and cognitive radio access technologies for 5G and beyond Plain OFDM (V,L) = (2,8) (V,L) = (2,32) (V,L) = (4,8) (V,L) = (4,32)

Pr(PAPR>PAPR0)

10–1

10–2

10–3

7

8

9

10

11

PAPR0 (dB)

Figure 4.10 The effect of block sizes in [17] on the PAPR Alignment signal

CP

Transmitter

TX data

Channel coding and interleaving

Mod.

S/P

IFFT

+

OFDM

P/S

Add CP

Add alignment signal

RF front-end

Channel

A S

+

OFDM

RX data

Channel decode and deinterleave

Demode

P /S

FDE

Noise

CP

Receiver

FFT

S /P

Remove CP

+

RF front-end

Figure 4.11 The block diagram describing [20], the first alignment signal technique

signaling, respectively. However, this hybrid waveform, whose design philosophy is embodied in Figure 4.11, uses the time-domain OFDM waveform and the channel state information (CSI) to synthesize such a signal that when superimposed on the whole time-domain signal suppresses its OOBE and aligns to the CP interval

Flexibility through hybrid waveforms

109

at the receiver, not affecting reception performance. This technique does not have the disadvantages described in earlier techniques. The technique was combined with the popular N -continuous OFDM [21] waveform to demonstrate that the idea can be generalized to other OFDM derivatives [22] and enhance their self-interference properties. The next iteration [23] added PAPR reduction to the original technique by modifying the objective function. The community’s response to these iterations was that the assumption of perfect CSI knowledge at the transmitter is in general impractical, which prompted the evolution of the static CP alignment technique family [24] that uses a separate alignment filter at the receiver that aligns the alignment signal to the CP interval, decoupling the technique from CSI knowledge. The application of an additional filter on top of the channel requires the CP to accommodate not only the channel impulse response (CIR), but also the alignment filter which requires a non-negligible number of additional taps. This fact prompted the researchers to come up with an additional dimension to align the signal to, without requiring additional filtering operations at the receiver while maintaining robustness against channel estimation errors. These requirements pushed researchers to arrive at the ultimate evolution of this scheme, if the focus is solely on OOBE and PAPR reduction [25]. This final iteration, dubbed joint time–frequency alignment, utilizes the CSI at the transmitter to not only align the signal on the CP after it passes through the multipath channel but also to allocate carriers experiencing deep fades that are unreliable to convey information [26] to carry the tones for the alignment signal. Such joint exploitation of the time and frequency responses of the channel, as demonstrated in Figure 4.12, enables robust practical designs against imperfections while improving the room for suppression.

Frequency

Time Alignment signal

CP

+

OFDM Transmitter

TX data

Channel coding and interleaving

Mod.

S/P

Mapping

IFFT

Add CP

Add alignment signal

RF front-end

Channel

CFR

P/S

Time

Frequency A S

AS aligned on faded subcarriers & guards

+

OFDM CP

Receiver

RX data

Channel decode and deinterleave

Demode

P /S

FDE

De mapping

FFT

S/P

Remove CP

Noise

+

RF front-end

Figure 4.12 Block diagram of joint time–frequency alignment described in [25]

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Flexible and cognitive radio access technologies for 5G and beyond

The dedication of carriers solves a major problem experienced in the original schemes: the alignment signal power profile reduction toward the middle of the symbol duration as the conditions necessary to satisfy the cancelation of interference from preceding samples and to the successive samples become too stringent, especially for fast decaying channels. This significantly reduces signal design flexibility and the PAPR reduction performance in particular, as the possible power reduction to the center sample is limited by the profile. As Figure 4.13 demonstrates, the allocation of alignment tones not only allows for a more uniform power profile for the alignment signal and increases the possible power, but such decoupling also eliminates the dependence on channel power delay profiles. This allows the technique to provide consistent and predictable gains under any channel conditions. The resulting PAPR reduction improvement is demonstrated in Figure 4.14. This additional room created significant gain over the PAPR reduction performance over the conventional CP alignment algorithm, outperforming cancelation carrier insertion as well. These gains also propagate to the OOBE reduction properties as seen in Figure 4.15. Returning to the investigation of the key performance indicator of original motivation behind this evolutionary step, which was to enhance robustness against channel estimation errors, the BER performance under channel estimation errors would provide sufficient information. As can be seen in Figure 4.16, errors in channel estimates

0 –5

D=0

D = 0.5

D=1

Power (dB)

–10 –15 –20

–25

OFDM AS-CPA AS-JTFA

100

50 Time samples

Figure 4.13 Power profile of plain OFDM, and alignment signals for CP alignment and joint time–frequency alignment for different channel decaying factors D

Flexibility through hybrid waveforms 100

111

Plain OFDM G = 0.5

Pr(PAPR>PAPR0)

G = 0.9

10-1 G = 0.98

CPA-OFDM CCI-OFDM JTFA-OFDM 3

4

5

6

7

9

8

PAPR0 (dB)

Figure 4.14 PAPR characteristics for conventional CP alignment-OFDM (CPA-OFDM), joint time–frequency alignment-OFDM (JTFA-OFDM) and cancelation carrier insertion-OFDM (CCI-OFDM) [27], where G ∈ [0,1] is a scale of importance favoring PAPR over OOBE reduction in the joint optimization 0 –5 –10 G = 0.98

G = 0.9 G = 0.5

Power spectral density (dB)

–15 Plain OFDM

–20

CCI+OFDM

–25

CPA+OFDM

–30

JTFA+OFDM

–35 –40 –45 –50 10

20

30

40

50

60

Subcarrier index

Figure 4.15 OOBE reduction performance comparison of conventional CP alignment, joint time–frequency alignment and cancelation carrier insertion [27], where G ∈ [0,1] is a scale of importance favoring PAPR over OOBE reduction in the joint optimization

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Flexible and cognitive radio access technologies for 5G and beyond

10–1

MSE = 0.2

BER

10–2

MSE = 0.05 MSE = 0

10–3 Plain OFDM CPA-OFDM JTFA-OFDM CCI-OFDM

10–4

5

10

Flat fading

15

20

25

Eb/N0 (dB)

Figure 4.16 The BER performance comparison of conventional CP alignment, joint time–frequency alignment and cancelation carrier insertion [27] for various MSEs in the channel estimate

do degrade the BER performance of the system; however, the degradation is not harsher than the degradation of plain OFDM. In conclusion, the alignment schemes improve the power efficiency, hardware requirements and spectral localization characteristics of OFDM signals while preserving the conventional OFDM structure, hence backward compatibility and orthogonality to legacy structures. Although the schemes are also utilized in other modulation schemes such as OFDM with index modulation (OFDM-IM) [28], these are discussed under Chapter 6 devoted to this modulation. The latest iteration of this family also focuses on enhancing the security of the waveform and is discussed in Section 4.2.2.

4.2.2 Secure OFDM Physical layer security (PLS) concepts and metrics [29] are discussed in detail in Chapter 18. The security requirements presented therein can be satisfied using a plurality of approaches, some of which are discussed in detail in other chapters of this book. In Chapter 18, providing secure communications through spatial diversity, particularly, through the use directional modulation [30], orthogonal space time block codes precoding [31], singular value decomposition-based multiple-input multipleoutput [32,33] are discussed. Exploiting medium access control layer functions [34] such as automatic repeat request [35], further in conjunction with the artificial noise (AN) concept discussed in Section 4.2.1.4 [36] for PLS, is discussed in detail.

Flexibility through hybrid waveforms

113

Network-level solutions [37] that use helper nodes that interfere with the adversary while amplifying and forwarding the information to the legitimate receiver that are useful in security critical power limited situations [38] are discussed. Although suitable for this section, adapting OFDM derivatives such as OFDM-IM for PLS [39] and proper subcarrier selection therefore [40] is discussed in Chapter 6 as the derivative waveform will be explained therein. This subsection presents advanced schemes that enable not only secure, but also spectrally efficient broadband communication also satisfying the low-latency requirements of beyond 5G applications in accord with Chapter 1 in particular using OFDM signaling structure. It was previously discussed in Chapter 2 that OFDM is the widely accepted waveform used in numerous standards to date. Many major candidates discussed in Chapter 2 preserve the OFDM structure and can be considered its derivatives, preserving the edifice for future standards to come [7]. Even without CP, OFDM signal structure can be exploited to locate transmitters by ranging [41]. A plurality of advanced waveform algorithms were introduced to mitigate this vulnerability. AN concept discussed in Section 4.2.1.4 can be injected in the fading channels of legitimate user to confuse the eavesdropper [42]. However, this allocates resources and consumes energy in order to provide security, which may be infeasible for low-power Internet of Things (IoT) devices [43]. Pilots can be manipulated to degrade the eavesdropper’s channel estimation ability without allocating resources and consuming power for security [44]. Alternatively, intentional inter-carrier interference (ICI) introduction to be compensated by the carrier frequency offset (CFO) of the legitimate receiver can be a less computationally complex method to improve security [45]. Interleaving in-phase and quadrature components to independently fading channels further reduces computational complexity [46], while adapting this interleaving operation to channel conditions greatly enhances SE while adding little computational complexity [47]. The precoded-OFDM schemes described in Section 4.2.1.2 can also be adjusted to provide secrecy without allocating additional resources [48]. Despite its popularity, CP has long bothered not only SE maximalists as a rate- and latency-limiting redundancy [49, Refs. 17–33], but also PLS aficionados due to the autocorrelation properties it introduces to the resulting signal [50, Refs. 9–15]. The cyclostationarity of CP-OFDM structure has historically been exploited to eavesdrop illegal or enemy communications [51]. Adapting CP size [52, Sec. 4.2] as a function of channel frequency correlation [53] was proposed to address both SE and security concerns simultaneously. However, the quasi-static nature of the channel limited the secrecy gain by allowing buildup of autocorrelation over periods for which the channel remains static, which pushed researchers into shifting the CP selection region in a pseudorandom manner to eradicate the cyclic features [54]. This purely PLS oriented approach had severe SE implications and evolved to limit the damage to SE by pseudorandomly changing the fast Fourier transform (FFT) size [50]. Those bothered by the SE problem went on to invent new spectrotemporally localized waveforms having low PAPR that collect and utilize the energy leakage caused by multipath channel as opposed to discarding this energy with the CP as done in conventional CP-OFDM [55]. The scheme also proved to be secure as the signals are

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Flexible and cognitive radio access technologies for 5G and beyond

transformed using the intended receiver’s channel contrary to OFDM that utilizes the publicly known Fourier basis [56]. While this approach has started getting traction as a strong candidate for future implementations, our focus in this chapter remains on the strongest candidate to date, OFDM. Instead of finding new saddle points between SE and PLS within the CP-OFDM framework by playing with CP selection and duration, the concept of channel shortening, which was originally proposed to mitigate channels with extreme delay spreads (DSs), was introduced to jointly enhance both SE and PLS [57]. In this section, we review the latest evolution of this approach in detail: using AN introduced in Section 4.2.1.4 to eliminate the need for CP redundancy by partially pre-equalizing the signal, greatly enhancing both PLS and SE in addition to reducing the latency while not adding any receiver complexity [49]. The AN is overlapped with the whole time-domain OFDM signal at the transmitter and, is designed to, upon convolution with the desired user’s channel, results in a circular convolution for the symbol in interest and cancels the inter-symbol interference (ISI) from the preceding OFDM symbol. As ISI is canceled, conventional one-tap equalizer is used at the receiver, maintaining the receiver complexity of conventional CP-OFDM. The transceiver structure of CPless OFDM is diagrammed in Figure 4.17. As also visualized in Figure 4.17, the AN spans the whole N inverse FFT (IFFT) samples at the transmitter. However, upon passing through the Rth-order multipath channel, it creates a circular leak to the first R samples of the received symbol in interest while removing the ISI caused by the samples of the preceding OFDM symbol and its AN to the same region. Note that the AN is not designed to prevent leakage to the next symbol, which allows the relaxation of restrictions at the end of a packet, similar to the notion of CP. Such a scheme

Alignment signal

+

OFDM Transmitter

TX data

Channel coding and interleaving

Mod.

S/P

Add alignment signal

IFFT

RF front-end

P/S

Channel

A S

OFDM Noise

Receiver

RX data

Channel decode and deinterleave

Demode

P/S

FDE

FFT

S/P

+

RF front-end

Figure 4.17 Transceiver structure of CP-less OFDM and visualization of temporal signal structure at the transmitter and after passing through the Rth-order multipath channel

Flexibility through hybrid waveforms

115

requires perfect CIR knowledge at the transmitter. This brings some limitations to the applicable scenarios. The scheme is applicable to low-mobility time division duplex (TDD) systems, so that the channel does not change significantly between consecutive estimates at the transmitting device [58]. The secrecy provided by CP-less OFDM is demonstrated in Figure 4.18. As expected, the scheme proves more secure than CP-OFDM and the secrecy is largely independent of DS. However, the increase in BER should not discourage the use of this scheme, as not employing CP duration allows conveying the information without redundancies, greatly improving the transmission efficiency as shown in Figure 4.19. This advantage in transmission efficiency remains valid even for shorter DSs, even if the CP is allowed to scale with the channel which is not allowed in current standards. As Figure 4.19 shows, a 6-dB decrease in DS drastically affects the transmission efficiency of CPOFDM scheme, whereas CP-less OFDM maintains the same performance regardless of the DS. Designing the AN as a function of the channel estimate brings to mind whether the scheme is usable in imperfect channel estimates. Figure 4.20 demonstrates that while the intermediary value changes with the amount of error in the channel estimate, the error floor at the target BER is achieved at the same SNR value. All in all, although the identified in-band interference demonstrated that the scheme still has room for improvement and is not perfect, this room for improvement will pave the way toward less redundant, more efficient, more latent and most importantly more secure future communications systems for beyond 5G applications.

BER

10–1

10–2

10–3

CP-less OFDM – legitimate receiver –DF = 1 CP-less OFDM – eavesdropper – DF = 1 CP-less OFDM – legitimate receiver – DF = 2 CP-less OFDM – eavesdropper – DF = 2 CP-less OFDM – legitimate receiver – DF = 3 CP-less OFDM – eavesdropper – DF = 3 CP-OFDM – legitimate receiver and Eavesdropper at any DF value 5

10

15 Eb/N0 (dB)

20

25

Figure 4.18 BERs of legitimate receiver and eavesdropper against SNR for CP- and CP-less OFDM at different channel DFs

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Flexible and cognitive radio access technologies for 5G and beyond 2.0

Transmission efficiency (bps/Hz)

1.8

1.6

1.4

CP-less OFDM – CDS = 1/4

1.2

CP-OFDM – CDS = 1/4 CP-less OFDM – CDS = 1/2 1.0

CP-OFDM – CDS = 1/2 CP-less OFDM – CDS = 1/8 CP-OFDM – CDS = 1/8

0.8 10

5

15 Eb/N0 (dB)

20

25

Figure 4.19 Transmission efficiency comparison between CP-less OFDM and CP-OFDM with different channel delay spreads for unit DF

BER

10–1

10–2

CP-less OFDM (mse = 0) CP-OFDM (mse = 0) CP-less OFDM (mse = 0.01) CP-OFDM (mse = 0.01) CP-less OFDM (mse = 0.05) CP-OFDM (mse = 0.05) 5

15

10

20

Eb/N0 (dB)

Figure 4.20 BER comparison between CP-less OFDM and CP-OFDM with various imperfect channel estimation error levels

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117

4.2.3 Beyond spectral localization: partially overlapping waveforms Most discussions in Section 4.2 focus on improving the spectral localization of the OFDM signal. But assuming that the signal could actually be localized to a finite set of time–frequency–space resources would stack them without any overlaps satisfy the insatiable thirst for resources? Furthermore, the discussions in the preceding section only took into account the impact of the waveform on the users within a cell, in the absence of other cells. But what is the impact of being in multicell networks on the capacity and what can be done in waveform level to address possible problems? Another group of researchers that were bugged by these questions took another approach to this problem. The discussion starts from the fact that, to date, an OFDM signal with a perfect time–frequency–space localization cannot be formed. This implies that no matter how much guard is inserted between resources in each domain, resources will inevitably overlap upon reception. Then, similar to the approach taken in the design of the Global System for Mobile Communications signal, it would be more beneficial to control how the overlapping happens rather than treat it as unexpected, as doing it controllably would enable possible solutions to the emergent issues. The concept began to manifest itself as a problem for the first time in 2009 in the context of cognitive radios. Since LUs in an orthogonal frequency division multiple access (OFDMA) system are synchronized such that the OFDM symbols are synchronized to temporally align at a BS, the timing between symbols would be misaligned at any other location, including that of the opportunistic secondary users’ cognitive radios [59], as seen in Figure 4.21a. This causes, as seen in Figure 4.21(b), a partial overlapping of OFDM symbols transmitted by each user. Hence, taking the FFT at any instant would cause ICI and ISI, effectively preventing the identification of unused resources for usage opportunities. Analysis and according possible solutions were proposed in [59] to enhance conventional cognitive radio approaches, until the problem resurfaced in the context of then-emerging heterogeneous networks (HetNets) [60]. At this point, it became obvious that it is no longer possible to scalably solve the issue if it is not controlled. The feasibility of allocating partially overlapping channels to satisfy the cellular resource hunger to some extent was first demonstrated in a single-cell network [61]. While investigating, the use of partially overlapping non-orthogonal resources as a means to improve throughput in the waveform domain did not remain limited to OFDM and spread to filtered multitone (FMT) [62], the investigation on cellular aspects such as modeling cellular parameters in overlapping cell scenarios continued to evolve as well [63]. The waveform domain approaches continued to evolve to modifying the filters of each user such that the minimum SE in the network is maximized [64]. The filter modifications later evolved to using filters that minimize overlapping with other user’s signals at the cost of increasing overlapping with the user’s own signals, which can be easily canceled [65,66]. In an effort to minimize co-channel interference on the system, [66] also introduces an intentional CFO to the desired user’s signal to avoid fully overlapping with the co-channel user utilizing another layer of the HetNet.

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Flexible and cognitive radio access technologies for 5G and beyond SU-2 senses available spectrum opportunities and MS 1 utilizes opportunistic spectrum access

Time

MS4

CP

SU-1

CP SU-1

MS-2

CP

MS2

Cause ICI

MS-3 CP

SU-2 Primary network

MS-1

CP

BS

MS-4

Cognitive radio

MS3

(a)

(b)

Figure 4.21 (a) Cognitive radio scenario considered in [59] igniting the discussion on partial overlapping and (b) illustration of the timing misalignment problem experienced by secondary user (SU)-2 due to primary network users’ signals arriving after the CP of SU-1

Aggressor’s subcarriers

υ0

υ0

Aggressor’s subcarriers

f

υ0

Victim’s receive filters

Victim’s receive filters

υ0

υ0

f

Received interference power

υ0

υ0 (a)

f

υ0

υ0

Intentional frequency offset

υ0

f

Received interference power

f

υ0

υ0

f

(b)

Figure 4.22 Illustration of interference for (a) full and (b) partial overlapping tones

The basic idea is illustrated in Figure 4.22. If a user’s receive filter perfectly aligns with an interfering signal, as it happens in Figure 4.22(a), the interference power received from the other user will be much higher compared to the case where the receive filter is offset from the interfering signal as seen in Figure 4.22(b).

Flexibility through hybrid waveforms More other-user interference

More selfinterference

Δf i

υ0

τ0

(a)

Less other-user interference

Frequency

Frequency

Less selfinterference

119

Δt i (Misalignment)

Δf i

υ0

Time

τ0

Δt i (Misalignment)

Time

(b)

Figure 4.23 Illustration of trade-off between (a) less self-interference and more other-user interference and (b) more self-interference and less other-user interference Intentionally adding time and frequency offsets aside, manipulating the filter characteristics is also a flexibility of which results cannot be ignored. This is demonstrated in Figure 4.23. Figure 4.23 shows the adaptation of filter parameters to the instantaneous overlapping between the resources of different users. In the instance shown in Figure 4.23 one user synchronized to half symbol duration and half subcarrier away from the other user. In Figure 4.23(a), both users utilize a Gaussian waveform that is evenly distributed in time and frequency. In this case, both users evenly receive similar amounts of interference from their own signals and from the other user. However, if the waveform is modified to localize evenly in either domain (in the case presented in Figure 4.23(b) the domain of choice is time as multitap equalizers are more commonly described in this domain), the interference from the other user is traded off to an increased self-interference. Canceling the interfering signal of another user requires estimating the channel of the interferer, equalizing the effects of the channel before it can be estimated. However, signals causing self-interference are already estimated in the data reception process and the cancelation process involves a lesser number of variables compared to estimating the data of an interfering user. By carefully designing the networks Gabor lattice and the waveform parameters, hardto-cancel other user interference can be minimized in favor of self-interference that can be canceled easily. Figure 4.24 demonstrates that once the desired link reaches high enough SNR values to cancel the interference effectively, partially overlapping tones yield much higher average capacities for the network. Interested readers can check the further technical proofs of this work concerning the distributed optimization of the introduced offset and the conversion characteristics of selected filter parameters in these distributed approaches [67] which proves the existence of Nash equilibrium in a two-step game that selects these parameters. Chapter 15 describes an extension of this work involving fully overlapping different numerologies. Resource allocation and scheduling complexity and the effect on performance thereof in cognitive HetNets are addressed in [68] in a similar manner.

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2.4

OFDM NOFDM

Average capacity (bit/s/Hz)

2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 5

10

15 20 SNR (dB)

25

30

Figure 4.24 Average capacity of conventional OFDM and partially overlapping non-OFDM (NOFDM) in uncoordinated networks The latest evolution of this family, which demonstrates the use of reconfigurable antennas (RAs) within this framework, is presented in [69], and directive dense IoT networks is presented in [70]. Readers unfamiliar with RAs and game theory can refer to [71] for basics of subcarrier allocation and power control in RAs using S-modular games; [72] for more about the application of such to multiple primary and secondary networks and [73] to learn more regarding the impact of RA state selection on the network capacity and interference amounts when antennas are used, all of which form the basis of [69]. Background information regarding these topics are also provided in this book in Chapters 8 and 14.

4.3 Waveform multiplexing approaches for beyond 5G RATs Section 4.2.3 discussed the impossibility of synchronous multipoint-to-multipoint communication and ways to resolve this unavoidable problem. Going back one step, is there a better solution available in the point-to-multipoint and multipoint-to-point synchronizable communications, or is the best option really multiplexing OFDM numerologies to adjacent channels, creating the problems discussed in Chapter 3? This section discusses other possibilities and motivations behind them.

4.3.1 Time-domain OFDM numerology multiplexing As discussed earlier in Chapter 3, the need for multiple numerologies mostly arises due to the large variation in channel conditions of users connected to femtocells to users connected to macrocells. There are a plurality of methods to estimate user

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121

location and motion statistics within a cell [63]. For example, [74] proves that the time and frequency spreading characteristics of a cell can be effectively modeled, and as accommodating every single value would result in unbearable overhead and control signaling, optimal values that ease spectrotemporal alignment while effectively accommodating a number of users in the most efficient manner can be formulated. As point-to-multipoint signaling and multipoint-to-point signaling can be synchronized in the time–frequency–space continuum, any finite duration signal would remain orthogonal to any other finite duration signal that it is not overlapping with. Given the finite-duration infinite-bandwidth nature of OFDM signaling, if the signals are serialized and time multiplexed, there would not be any INI, and multiple numerologies can be used together without any interference issues. A simple example of such a slot of a frame is provided in Figure 4.25, wherein users are grouped based on their channel conditions, each group is assigned a subcarrier spacing and CP duration and the generated CP-OFDM symbols are multiplexed in time, maintaining frequency orthogonality. The spectrum licensing specifies almost 100 spectrum fractions globally for use, some of these fractions have a very narrow band and are located in lower carrier frequency ranges, while others facilitate large amounts of spectrum in higher carrier frequencies as a single continuous chunk. Instead of multiplexing users requiring different numerologies to adjacent bands within these large chunks, the authors of this chapter find it more sensible to utilize the narrower fractions for user groups that do require little data overall and move users to wider band spectrum fractions as the total amount of requested resources increases [75]. Avoiding INI with such an approach may prove to be more efficient than allocating resources in a way that results in severe INI, then trying to mitigate this synthetic issue.

Short TCP

Large Δf

Long TCP

Large Short TCP Δf

...

...

Small Δf

Long TCP

Small Δf

...

Multiuser aware frame structure

f

t

Fixed TCP and Δ f

Fixed TCP and Δf

... ...

... ...

Conventional frame structure

f

t

K OFDMA symbols

Frame 1

Frame 2

...

...

Frame #.

... t

Figure 4.25 Spectrotemporal comparison of multiuser aware frame structure to conventional frame structure

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Flexible and cognitive radio access technologies for 5G and beyond

4.3.2 FDM of OFDM numerologies against hybrid waveforms The previous subsection discussed how OFDM numerologies can be multiplexed only in time to avoid INI. This subsection asks and attempts to answer a different set of questions in case the arguments of the previous subsection are invalidated: if it is absolutely necessary to multiplex different waveforms to adjacent channels, and given that one of them is OFDM, is it the best approach to use OFDM with different parameters for the second waveform just because similar structures share certain characteristics [7]? Let us delve into the specifics of the question. OFDM provides the perfect time localization, which in turn proves it with relevant characteristics such as robustness against DS and the minimum latency possible [76,77]. But the structure is inherently weak against frequency dispersions and has high PAPR. These characteristics are summarized in Figure 4.26. While some low-mobility wideband services such as enhanced mobile broadband (eMBB) would appreciate the strengths of OFDM, as evidently done in cellular and wireless local area network standards, other services that put PAPR above all else like IoT or require robustness against frequency dispersions such as ultra-reliable low latency communication (URLLC) are employing the worst possible modern design by utilizing OFDM waveform [78]. Some others require using a fraction in the spectrotemporal resource grid that is inaccessible by OFDM due to its spectral in containment. On the other hand, using another waveform such as RC FMT allows highly flexible signal design that can be shaped to fit in to various opportunities in the spectrum as seen in Figure 4.27.

Time localization

CP rate

Filtering/ windowing

Spectral efficiency

Subcarrier spacing

OFDM

PAPR

Robustness to time dispersions

Latency

Filter localization in time and freq.

Number of subcarriers

Filter length

FBMC

Frequency localization

Robustness to freq. dispersions

Figure 4.26 The various parameters of OFDM and FBMC waveforms and the metrics they primarily affect

Flexibility through hybrid waveforms .

=

f/F

f/F

f/F

f/F

t/T

t/T

Frequency localization ↑ PAPR ↑

t/T

f/F

=

=

t/T

= f/F

PAPR ↑ Latency ↑ Frequency localization↑

Time localization ↑ Intersymbol interference ↑

=

123

t/T

t/T

Time localization ↑ Robustness against delay spread ↑

Figure 4.27 Parameters of RC filters and their impact on the signal properties shown along with ambiguity functions

For unfamiliar readers, the ambiguity function shows the cross-correlation, which also equals the absorbed energy, between a first waveform (usually referred to as the transmit pulse) and a spectrotemporally shifted second waveform (receive pulse). For example, the color being bright red, corresponding to 0-dB energy at origin, denotes that if the second pulse is not shifted in time and frequency, and accordingly all of the transmit energy is absorbed. A good example can be seen in Figure 4.28, which shows the ambiguity function between a half spacing subcarrier and a unit spacing subcarrier. As discussed in Section 4.3.1, as soon as the OFDM symbol is over in time, the whole spectrum becomes orthogonal to the first tone. For the same time instant, going an integer number of subcarriers away from the first tone also results in an orthogonal reception. Shifting to any other non-integer frequency and within symbol period time results in high absorption, with exceptional points present. Let us check what happens if the second tone is an FBMC subcarrier, say a root RC (RRC) FMT without roll-off. As seen in Figure 4.29, while time-domain multiplexing is not as interference free as OFDM–OFDM in a flexible manner, the FBMC pulse can still be placed integer unit symbol durations apart, and this would result in an orthogonal system. The orthogonality is still preserved also for integer unit and a half symbol durations and more than unit subcarriers apart, as well as half subcarriers apart with perfect time synchronization. To observe the effect of filter parameters, we can check Figure 4.30 and compare with Figure 4.29. Using the half-cosine filter instead of the sinc filter greatly reduces the interference with the sinc in both time and frequency, with the exception of immediately adjacent bands, due to the bandwidth expansion of the half-cosine.

124

Flexible and cognitive radio access technologies for 5G and beyond 3

0 –10

2

–20 1

F

–30 0 –40 –0

–50

–2

–3 –3

–60 –70 –2

–1

0 T

1

2

3

Figure 4.28 Ambiguity function of two OFDM subcarriers, first having half unit subcarrier spacing and the second having unit subcarrier spacing 3

0 –10

2

–20 1

F

–30 0 –40 –0

–50

–2

–3 –3

–60 –70 –2

–1

0 T

1

2

3

Figure 4.29 Ambiguity function of an OFDM subcarrier and an RRC FMT without roll-off, OFDM subcarrier half unit subcarrier spacing The advantages of varying waveform parameters at different spectrotemporal distances from the neighboring numerology spawn the idea of hybrid waveforms and frames. In line with Section 4.2, the characteristics of the waveform can be manipulated within the same numerology to accommodate certain features, which

Flexibility through hybrid waveforms 3

125

0 –10

2

–20 1

F

–30 0 –40 –0

–50

–2

–3 –3

–60 –70 –2

–1

0 T

1

2

3

Figure 4.30 Ambiguity function of an OFDM subcarrier and an RRC FMT with unit roll-off, OFDM subcarrier half unit subcarrier spacing are provided in Chapter 2. A few examples are presented therein, wherein the window duration of the OFDM numerology is increased toward the band edges to better localize the power spectral density (PSD) as seen in Figure 4.31. This results in a reduced CP duration for the resource elements (REs) at the edges; however, by carefully scheduling the UEs having shorter DSs to those REs, no performance degradation will be experienced. While windowing, all of the subcarriers in a W-OFDM system may be the optimum, as some REs cannot accommodate this function, a smart utilization of the correct resources at hand provides an edge. The time-domain analog applies to FBMC as seen in Figures 4.32 and 4.33. While utilizing a higher roll-off requires a larger bandwidth overall and results in long temporal tails, by increasing the roll-off only at the symbols at the slot edges, the temporal localization can be greatly enhanced while maintaining a narrower bandwidth overall. Combining the contents of the whole chapter together, one can reach the flexible hybrid frame designs shown in Figure 4.34. The first frame is compatible with 5G OFDM numerology multiplexing, utilizing flexibilities such as windowing and filtering of the edge carriers to localize the waveforms, as well as all other algorithms described in Section 4.2. Once again, the authors do not endorse spectral multiplexing of OFDM numerologies to adjacent channels, temporal multiplexing seems to be a more viable option. The second frame is a strong candidate for beyond 5G frames, wherein the base waveform does not only derive from OFDM but other waveforms such as FBMC are allowed in the system, as they provide better intercompatibility than other OFDM numerologies and cause less INI. Windowing and filtering the edges by smart allocation of REs are promoted in an effort to reduce spectrotemporal redundancies.

126

Flexible and cognitive radio access technologies for 5G and beyond 0

–10

PSD (dB)

–20

–30

–40

–50

No windowing Edge windowing Reg. windowing (α = 0.1)

–60

–70 0

20

40

60

80

100

120

f/F

Figure 4.31 PSDs of conventional, edge windowed and windowed OFDM

4.4 Numerology-based scheduling Earlier sections of this chapter and Chapter 3 have shown that multiplexing of numerologies creates new problems such as INI. At some point, it is necessary to investigate the feasibility of the level of personalization in numerology designs due to their incompatibilities. In other words, there is an heretofore unresolved need for a mechanism, or even a metric to evaluate the efficiency of such mechanism, that determines whether it is more beneficial to allocate alternative numerologies given how different numerologies can be properly mapped to the frame [79]. In this subsection, attempts in finding such a metric and an efficient method for numerology election is discussed. The first step in the quest to formulate the maximization metric is to identify and quantify the interactions between requirements and limitations of elements belonging to different Open System Interconnection layers. A bold and comprehensive taking on this quest is exemplified in Figure 4.35 [80]. The need for various numerologies stems mostly from user elements, service promotes a number of conditions, and the numerologies are limited by the capabilities of the network operator on which design decisions need to be made [81]. Design decisions can neither be exclusive from one another and are heavily coupled to the wireless channel. In addition to

Edge − windowed OFDM numerology

Edge − filtered FBMC numerology

More windowing

Sharper wiltering

127

CP Window

Time

Flexibility through hybrid waveforms

Frequency Figure 4.32 Flexible OFDM and FBMC numerology design examples

0 –10

Power (dB)

–20 –30 –40 –50 RRC (α = 0) RRC edge filtering RRC (α = 0)

–60 –70

0

20

40

60 t/T

80

100

120

Figure 4.33 Magnitude responses of sinc, RRC edge filtered and RRC FMT

Flexible and cognitive radio access technologies for 5G and beyond Frequency

128

Frame-1: Mixed OFDM numerologies

(

11 )

(

12 )

(

13 )

(

14 )

(

15 )

Frame-2: Hybrid OFDM/FBMC numerologies

(

21 )

(

21 )

(

22 )

(

23 )

(

22 )

(

24 )

(

23 )

Time კ= Windowed CP

{ , } = Edge filtered OFDM subcarrier კ

კ

{კ, } = Edge filtered FBMC symbol

{■, ■, ■, ■} = CP კ

კ

{ , } = Edge filtered FBMC subcarrier

{■, ■, ■, ■} = FBMC numerologies კ

{■, ■, ■, ■, ■} = OFDM numerologies

Figure 4.34 Single waveform flexible multi-numerology and multi-waveform flexible multi-numerology frames

the relations amongst multiple layers, the relations within these elements cannot be ignored. For instance, UEs may have multiple nonexclusive service requirements at once as demonstrated in Figure 4.36 [82]. Note that it may not always be possible to satisfy every requirement of all users simultaneously with finite resources. In such a case, the scheduler must direct its efforts to instead proactively satisfying the overall system. This requires careful tailoring of a plurality of goal functions shown in Figure 4.37. The many flexibilities provided by beyond 5G systems create an immerse number of different numerology options. At the same time, the number of variables involved in the problem at hand is too many even if a single user is considered, too. Attempting to map such a large number of inputs to find the optimum output in a vast number of possibilities is not feasible. The problem can be simplified and made scalable by first limiting the number of possible outputs, i.e., numerologies that users can be mapped to. This tiered numerology assignment approach is demonstrated in Figure 4.38. Given that classifiers are now used to robustifying OFDM receivers [83] and even as receivers themselves [84], such a classifier can be trained to use features that combine the general state of the network to identify optimal conditions. A simple

Flexibility through hybrid waveforms

Environmental effects

129

Waveform design

Hardware impairments

PHY design System requirements

Service requirements

Channel structure

MAC/frame design

Resource constraints Network operator requirements

User perspective Service perspective

Network design

Mutual interaction Unilateral interaction

Network operator perspective Design perspective

Figure 4.35 Interactions between requirements and limitations of elements utilized in numerology selection and scheduling algorithms

example of such a feature set is given in Figure 4.39. After a general structure that improves the overall system satisfaction is determined by the classifier; details are fine-tuned by a second layer of algorithms, which evaluate the feasibility of applying waveform processing techniques described in the previous sections so that a more optimal numerology, both for the user and the network, can be selected. The general structure comprises system-level parameters such as the number of total numerologies, total system bandwidth, guard bands between numerologies and feasible waveform processing techniques that would address user’s needs and so on. While even 5G can easily yield more than 1,000 possible general network structure classes, a simple 10 class general network structure classifier and its classes are provided for the readers convenience in Figure 4.40. Once the general network structure is identified, user numerologies can be tailored. Consider that the previously described classifier was provided with 10 users having 10 distinct power levels and that classified this as a two-numerology network. Furthermore, let us assume that the users are assigned to belong to one of the numerologies, as shown in Figure 4.41.

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Flexible and cognitive radio access technologies for 5G and beyond

UR LL

UR LL HS

HS HSE

HSE UR HS LC HEE

HSE LL HS

HEE: High-energy efficiency

HS: High security

LL: Low latency

HSE: High-spectral efficiency LC: Low complexity

UR: Ultra reliability

Figure 4.36 Nonexclusive requirement combinations of different services

Not enough resource or capacity!

Complete satisfaction for all users?

Contradiction!

Goal function 1 Fully satisfied users

Overall system satisfaction is not provided!

(a)

Goal function 2 Overall system satisfaction

Optimization for overall system

(b)

Figure 4.37 Flowchart between tiered multiple-goal functions, wherein if (a) primary goal: “fully satisfy all users”; is not achievable, the scheduler pursues (b) secondary goal: “satisfy overall system”

Flexibility through hybrid waveforms

User info Channel info Service type

Machine learning model

131

General structure for the configurable waveform parameters

Applying waveform processing techniques (windowing, filtering, guard utilization, etc.)

Service type

User info

Numerology assignments (subcarrier spacing, CP duration, etc.)

Channel info

(a)

Algorithms

(b)

Figure 4.38 Tiered system structuring and numerology assignment approach, wherein (a) basic inputs of all users are used to obtain a general network structure class followed by (b) a fine-tuning of per-user parameters within the general structure

Let us now demonstrate several exemplary ways to multiplex users within their bandwidth parts. The outer edges of bandwidth parts are proven to be more susceptible to INI [85]. Lower powered users can be assigned inner fractions of the bandwidth part to protect them from INI where they are surrounded by users that are orthogonal to them. Once lower powered users are secured in the inner numerology domain fraction, remaining users can be multiplexed to outer numerology domain fractions depending on their channel gains at these respective frequencies. This division of numerology domains into fractions and protecting the vulnerability by placing them in the inner fraction is referred to as fractional numerology domains (FNDs), which is shown in Figure 4.42 [86]. Note that this algorithm can also be interpreted in the cellular domain. Consider the two centric ring network shown in Figure 4.43 [86]. Users utilizing both numerologies are both inside the cell center and the cell edge. Users at the cell edge

132

Flexible and cognitive radio access technologies for 5G and beyond Raw dataset (Uxl elements)

Feature extractor

Feature-1

Mean of maximum excess delay for all users.

Feature-2

Variance of maximum excess delay for all users.

Feature-3

Mean of maximum Doppler effect for all users.

Feature-4

Variance of maximum Doppler effect for all users.

Feature-5

The number of users for eMBB service.

Feature-6

The number of users for URLLC service.

Feature-7

The number of users for mMTC service.

Figure 4.39 Example feature extractor for general network structure classifier

Figure 4.40 Class details of an example 10-class general network structure classifier are more susceptible to power level degradation and URLLC users are, susceptible or not, of critical importance [87]. In this scenario utilizing two numerologies, the whole half farther from the utilized bandwidth can be considered inner. Cell-edge users and critical URLLC users [88] are assigned these frequencies [89].

Flexibility through hybrid waveforms

133

UE 1 NUM 1 PL 1

UE 2 NUM 2 PL 2

UE 8 NUM 2 PL 8

UE 9 NUM 1 PL 9

UE 3 NUM 2 PL 3 UE 10 NUM 2 PL 10

UE 6 NUM 2 PL 6

UE 4 NUM 1 PL 4

UE 7 NUM 1 PL 7

UE 5 NUM 1 PL 5

Power level (PL)

UE 1

UE 4

UE 7

UE 5

UE 8

UE 9

UE 3

UE 2

UE 6

UE 10

BWPs with NUM-2

BWPs with NUM-1

Frequency

Figure 4.41 An example ten-user network classified to use two numerologies with the shown user assignments Numerology-2

Numerology-1

Edge users

Non-edge outer users

Non-edge outer users

Edge users

Edge Non-edge users outer users

Non-edge Edge outer users users

.. .

...

Frequency Outer users (candidate edge users)

Inner users

Outer users (candidate edge users)

Guard band

Outer users (candidate edge users)

Inner users

Outer users (candidate edge users)

Figure 4.42 FND structure For the outer users, decision on who goes on the numerology edge not only depends on power level, but also the level of diversity utilized by the user [90]. Within outer users, users that require higher bandwidths would have more resources within the inner bands compared to users utilizing narrower bandwidths [91]. By extensive use of link adaptation, higher bandwidth users can pull their data rates higher when put on the edges, resulting in a fairer resource utilization [92]. This is referred to as the power difference-based (PDB) scheduling, of which flowchart is provided alongside that of FND in Figure 4.44.

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Flexible and cognitive radio access technologies for 5G and beyond

Region of cell center

URLLC

eMBB

URLLC Region of cell edge

mMTC

mMTC mMTC

eMBB eMBB

URLLC

eMBB

mMTC

mMTC URLLC

Inner users of NUM-1

...

Outer users of NUM-1 Outer users of NUM-2

Inner users of NUM-2

...

NUM-2

NUM-1

Frequency

Figure 4.43 Application of FND structure to co-centric cells

START

START

Check user feedbacks at BS.

Check user classes at BS.

YES

URLLC user?

NO

Any outer users? YES

NO NO

NO

Cell edge user?

Cell edge user?

YES

YES

Classify it as a lowpriority inner user.

Classify it as an other (or candidate edge) user.

Classify some inner users as outer users regarding their priorities.

Classify it as a highpriority inner user.

Classify it as a mediumpriority inner user.

(a)

Apply power difference-based scheduling algorithms.

Give a decision for the edge users.

(b)

Figure 4.44 Flowchart of (a) main algorithm for fractional numerology domain scheduling and (b) main algorithm for power difference-based user scheduling algorithms In conclusion, multitiered approaches are beneficial in minimizing the computational complexity of the numerology assignment and scheduling problems that are expected to grow exponentially in beyond 5G systems. Users share their needs with the BS which in turn decides on a general network structure within the available set to support the users, then assigns numerologies and schedules them resources. Note that as numerology selection flexibility increases, SE decreases due to the increase in

Flexibility through hybrid waveforms Numerology decision

Scheduling algorithm

135

User #3 Necessities

Base station

How can we balance various trade-offs while using the multi-numerology structures?

Numerology set

Numerology decision

Necessities

Flexibility

Spectral efficiency

Numerology decision

Necessities

User #2

Scheduling complexity Computational complexity

User #1

Control signaling overhead

Figure 4.45 An abstraction of multiuser numerology selection and scheduling along with relation among trade-offs affecting cardinality

guards to escape INI, the complexity of scheduling users among other users and waveform generation increases. The control signaling overhead necessary to communicate the choice from a vast number of possibilities also increases with the cardinality of this set. This phenomenon is visualized in Figure 4.45.

4.5 Conclusion While OFDM is favored due to many reasons, it has poor PAPR, OOBE and secrecy characteristics. Hybrid OFDM-based waveforms allow enhancing these characteristics while mostly reusing conventional frame structures. However, beyond the synchronous spectrotemporal characteristics, existence in a network requires certain interference management abilities, which are present in hybrid FBMC waveforms. Neither is perfect in all senses, and the right waveform must be chosen with the right parameters to maximize SE. Using only one results in severe interference in the system, which can be avoided if OFDM symbols are time multiplexed and FBMC symbols are frequency multiplexed. The interference and performance degradation in any scenario that requires any other multiplexing can be resolved by using the flexibilities of waveforms and numerologies, enabling future radio access technologies (RATs).

Acknowledgment The authors would like to express their gratitude to Furqan Madni for his help with comments to improve this chapter.

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Chapter 5

Generalized and flexible modulation options Ahmad M. Jaradat1 , Jehad M. Hamamreh2 , and Hüseyin Arslan1,3

Flexibility support is critical for fifth generation (5G) and beyond radio access technologies (RATs) to meet diverse applications, channel conditions, and user requirements. Besides the flexible waveform design discussed in Chapter 4, modulation design emerges as another degree of freedom that can be powerful for the design of 5G and beyond RATs. The basic relation of waveform with modulation is discussed in this chapter. A unified and inclusive framework is presented for various modulation options by exploring different dimensions, including index, number, shape, etc. Furthermore, some future modulation candidates are envisioned for 5G and beyond RATs.

5.1 Introduction The 5G and beyond networks should meet a growing range of applications with different demands and features [1]. Thus, assigning the proper modulation for a specific use case is crucial, particularly for rigorous requirements, such as data rates, latency, and energy efficiency. The selected modulation option affects several strategies for the future communication systems comprising millimeter-wave wireless communications [2]. The incoming data bits could be conventionally mapped to traditional constellation symbols using the classical single-carrier digital modulations such as M -ary phase-shift keying (PSK) and M -ary quadrature amplitude modulation (QAM). In order to improve the data rate for a given communication system, the orthogonal frequency division multiplexing (OFDM) multi-carrier transmission scheme can be used. In conventional OFDM transmission, each constellation symbol is carried by an OFDM subcarrier. Differently, the conventional constellation symbols could be conveyed by making changes either between two consecutive subcarriers in the same OFDM symbol or the difference between two successive symbols in the same

1

Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Turkey Department of Electrical and Electronics Engineering, Antalya Bilim University, Antalya, Turkey 3 Department of Electrical Engineering, University of South Florida, Tampa, USA 2

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subcarrier [3]. This kind of transmission is called OFDM-differential modulation (OFDM-DM), which is characterized by its insensitivity to slowly changing amplitude and phase distortion; thus, the OFDM-DM transmission scheme enables the total elimination of equalization processes. In addition to the conventional and differential modulation schemes for OFDM systems, some other modulation schemes utilize a third dimension besides the 2-D signal constellation plan with the main aim of transmitting additional information. The utilized 3-D relies on the application, its requirements, and its capabilities. Each modulation technique is formed by its specific parameters, which constitute the flexibility aspect of modulation design. Spatial modulation-OFDM (SM-OFDM), OFDM with index modulation (OFDM-IM), OFDM with subcarrier number modulation (OFDMSNM), and OFDM with pulse superposition modulation (OFDM-PSM) are some examples of these 3-D emerging modulations, which are built based on some modifications in the OFDM-based waveform. The SM-OFDM [4] and OFDM-IM [5] transmission schemes are structured by the utilization of the index of active antennas and subcarriers, respectively, for OFDM-based waveform. Therefore, the SMOFDM and OFDM-IM schemes can be categorized under the index-based modulation family [6], in which the index of active transmit medium is exploited to send additional data bits. Another degree of freedom for efficient data transmission is the utilization of the number of active transmit resource(s) as another dimension for conveying extra information bits. OFDM-SNM scheme [7] is considered as one of the promising candidate number-based modulation schemes where the number of active subcarriers is exploited to transmit extra information bits beside the symbols from a conventional modulation alphabet. It is worthy to note that OFDM-SNM offers a unique feature related to enabling floating active subcarriers within an OFDM subblock so that they could be positioned in any index, unlike the fixed indices of active subcarriers featured in the OFDM-IM scheme. This feature in OFDM-SNM could be exploited to make the active subcarriers channel-dependent, which results in improved reliability performance. Moreover, the shape dimension could be exploited to enhance the data rate of the OFDM-based transmission scheme. The OFDM-PSM scheme is considered as one example of the shape-based modulation family where Np pulses are modulated based on the incoming bit sequence, and then they are superimposed to transmit Np data symbols in each grid of rectangular time–frequency lattice structure. Therefore, data rate could be enhanced to Np times. In [8], multiple orthogonal Hermite–Gaussian (HG) carriers have been used with rectangular orthogonal frequency division multiple access to boost the bandwidth efficiency. In this chapter, a unified and inclusive framework is presented for various modulation options. The basic relation between the modulation and waveform is provided in Section 5.2. The flexibility aspects in the modulation design are discussed in Section 5.3. Classification of the featured modulation options for 5G and beyond waveforms, especially the OFDM waveform, is described in Section 5.4. Index-based modulation options are explained in Section 5.5. Section 5.6 presents the number-based modulation options. In Section 5.7, the shape-based modulation family is discussed.

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Performance analysis of the featured modulation options in practical conditions, including a comprehensive comparison in terms of their spectral efficiency (SE), reliability, power efficiency (PE), out-of-band emission (OOBE), and computational complexity is presented in Section 5.8. Section 5.9 shows the applications of the considered modulation options for 5G and beyond networks based on the provided comparative evaluation. In Section 5.10, some other flexible modulation options for OFDM variants are discussed. Section 5.11 exhibits envision for futuristic modulation options for beyond 5G. Finally, the chapter is concluded in Section 5.12.∗

5.2 The relation between modulation and waveform in communication systems In recent research efforts related to 5G and beyond RATs, there have been many contributions in the development of new innovative methods and schemes related to the following two research directions at the physical layer level. ●

●

Advanced waveform designs: The purpose here is to create new improved waveforms (other than the conventional OFDM waveform) to enhance the system performance metrics in terms of peak-to-average power ratio (PAPR), OOBE, adjacent channel interference, inter-numerology interference, synchronicity, complexity, SE, reliability, robustness to inter-carrier interference, Doppler, phase noise, etc. Examples of waveforms in this domain can be shown in Section 2.3. New modulation techniques: The goal here is to come up with novel modulation schemes (other than the conventional M -ary QAM/PSK modulation schemes) to improve the system performance metrics in terms of SE, PE, and reliability. Examples of techniques in this domain are shown in detail in this chapter.

However, there has been some confusion in the literature in terms of naming, where many people think that waveform is modulation as can be seen in [9], which is not very true as they are related but different from each other. To understand the difference and see if there is any relationship between them, we will resort back to the most basic, fundamental definition as follows. Modulation is the process of intentionally varying the characteristics of radio environment (at the transmitter) in accordance with the information signal. This definition also involves the modulation as a general technique of shaping a signal to convey information. More specifically, the definition of modulation involves changing the properties of a physical signal in the analog domain according to the incoming information. For instance, when the used carrier signal in a digital communication system is a sinusoidal function, then its properties, including amplitude, phase, and frequency, can be changed according to the incoming information bits, resulting in what is called amplitude shift keying, PSK, frequency shift keying, respectively. This is well known in the literature as the most basic form of modulation. Also, another ∗ In this chapter, optical communication OFDM-based modulation techniques are not taken into consideration.

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possible definition of modulation is the mapping the incoming information bits to constellation symbols in the digital domain, such as binary phase shift keying (BPSK), then the transmission carrier changes state according to each symbol. As a new fronttier, researchers have recently used the properties of a subblock within an OFDM symbol structure as a new type of carrier at the baseband to send extra information bits. The subblock properties in terms of the indices and number of subcarriers within a subblock [10] are used to convey extra information. These changes in the properties of the physical signal contribute to the final physical signal, which represents the waveform that can be transmitted and received over a medium. The final physical signal occupies physical resources (like bandwidth, space, etc.) in multi-dimensional hyperspace. As defined in Section 1.3, waveform involves the process of generating the collective physical signal corresponding to multiple users and/or multiple information data that occupy the hyperspace. The waveform defines the locations of the transmitted signal, i.e., lattice points and spacing. Also, the waveform involves the way of transmission, for example, the physical shapes that contain energy in the hyperspace. The waveform involves any processing that can control these shapes. The waveform also involves the packaging of multiple user information in a hyper-spectrally efficient manner. It should be noted that anything added to the physical signal, such as noise, redundancy, and precoding process, is also part of the waveform. In brief, the waveform defines the physical shape of the signal that carries the modulated information through a channel. From the earlier discussion and explanation, the differences between waveform and modulation concepts are clearly stated and concluded. This description is expected to help researchers choose appropriate terms and names to the proposed schemes, methods, techniques, etc. according to their exact functionality to avoid any confusion in the basic understanding of new concepts.

5.3 Flexibility in modulation design Flexibility support is crucial for beyond 5G communication networks to meet different applications, channel conditions, and user requirements. In the literature, diverse modulation schemes have been proposed, and each of them offers different merits on different applications and channel conditions. Several merits cannot be achieved by the conventional modulation schemes, which necessitate the design of flexible modulation options. For example, the conventional modulation for the OFDM waveform is not flexible enough for future RAT, as discussed throughout this chapter. There is no optimal modulation that meets all user demands, i.e., each modulation scheme has some demerits in some scenarios. Therefore, it is worthy to investigate the flexibility perspective of a given modulation scheme by studying its parameters, which facilitates a proper selection of modulation techniques for future RATs [11]. Modulation design emerges as another degree of freedom to overcome different signal distortions and interference types. By doing a proper adjustment in the modulation parameters, more robustness against interference is expected, and the

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communication performance would be maintained. Moreover, different modulation schemes exhibit different inherent advantages. For example, the non-conventional modulation schemes for OFDM waveform, such as OFDM-IM and OFDM-SNM, have inherent robustness against inter-carrier interference (ICI) due to having some inactive subcarriers in their transmission block. These features are not observed in the conventional OFDM scheme where almost all subcarriers are active. Also, some modulation techniques offer inherent advantages under specific scenarios. For example, the OFDM-SNM scheme exhibits high throughput performance at low modulation orders due to its inherent feature of conveying the information via the number of active subcarriers. For providing more degree of freedom, there is a need for more flexible modulation techniques to address various technical challenges in the next-generation communication networks. The flexibility in the modulation design can be interpreted in modulation selection and parameter adaptation based on the channel and user conditions, which results in optimizing the communication performance for all users. Another flexibility that could be offered in beyond 5G is by designing a hybrid scheme that smartly combines different modulation schemes to provide various advantages with minimal complexity overhead.

5.4 Classifications of the modulation options for 5G and beyond waveforms The featured modulation options could be integrated with different waveforms by proper adjustments. The OFDM waveform is chosen as a basic waveform in our classification due to its wide use in different standards, technologies, etc. An OFDM frame refers to a number of consecutive OFDM symbols, and it consists of the M -ary modulation alphabet placed on the time–frequency plane. For the modulation options shown in Figure 5.1, the time-domain transmitted signal for an OFDM symbol can be represented as 1 xt = √ F−1 N x, K

(5.1)

where K represents the number of active subcarriers, F−1 N is the N -point inverse fast Fourier transform (IFFT) matrix, and x is the main frequency domain OFDM block T of N subcarriers: x = x(1) x(2) · · · x(N ) . Table 5.1 shows different formations of x for different modulation options. From a high-level viewpoint, the possible modulation options for OFDM-based waveform could be classified depending on the number of dimensions exploited by the modulation option. Figure 5.1 shows a possible classification where the classical OFDM and OFDM-DM exploit the complex 2-D signal plane, and the remaining modulation options exploit additional dimension alongside the constellation symbols. These multidimensional modulation schemes include index-based [14], number-based [7], and shape-based [8] modulation schemes.

148

Flexible and cognitive radio access technologies for 5G and beyond Differential OFDM 2-D signal representation

Conventional OFDM Temporal domain OFDM-STSK

Modulation options for OFDM-based waveforms

Index-based OFDM family

Spatial domain

SM-OFDM GSFIM-OFDM

Frequency domain

3-D signal representation

OFDM-IM

Code domain

IM-OFDM-SS

Channel domain

MBM-OFDM

Number-based OFDM family

OFDM-SNM

Shape-based OFDM family

OFDM-PSM

Figure 5.1 Classification of the modulation options for OFDM-based waveform. © 2019 IEEE. Reproduced, with permission, from [10]. Table 5.1 The formation of the frequency domain blocks for the featured modulation options Modulation option Conventional OFDM [12] OFDM-DM [13] SM-OFDM [4]

OFDM-IM [5]

OFDM-SNM [7] OFDM-PSM [8]

x T x = s(1) s(2) · · · s(N ) , where s(k) ∈ S, S represents the conventional constellations The information is conveyed in the difference between subsequent symbols over the same OFDM subcarrier or between subsequent samples within the same OFDM symbol The frequency domain block of the jth transmitting antenna: x = T xj (1) xj (2) · · · xj (N ) , which differs from frequency domain blocks for the transmit antennas whose indices are all integer numbers from 1 to NT except j x is built depending on ig , and sg , where ig = {ig,1 , ig,2 , . . . , ig,a } and T sg = sg (1) sg (2) · · · sg (a) , where sg (γ ) ∈ S. The number of active subcarriers of gth subblock is fixed Similar x formation as in OFDM-IM expect that the number of active subcarriers of each subblock varies based on the input data sequence The orthogonal HG pulses are modulated by different data symbols s(k) ∈ S

Generalized and flexible modulation options

149

Different domains, such as temporal and frequency, could be utilized separately or jointly for the featured OFDM-based modulation options. Examples of modulation options that exploit combined domains could be as follows: ●

●

●

OFDM-aided space–time shift keying (OFDM-STSK) [15]: Spatial and temporal domains are exploited in the index dimension. Generalized space frequency index modulation-OFDM (GSFIM-OFDM) [16]: Spatial domain and frequency domain are utilized in the index dimension. OFDM-PSM: Time domain and frequency domain are exploited in the shape dimension.

Moreover, examples of the modulation options that exploit individual domains could be as follows: ● ● ●

●

●

OFDM-IM: Frequency domain is exploited in the index dimension. SM-OFDM: Space domain is utilized in the index dimension. Index modulated OFDM spread spectrum (IM-OFDM-SS) [17]: Code domain is exploited in the index dimension. Media-based modulation (MBM) [18]: Channel domain is utilized in the index dimension. OFDM-SNM: Frequency domain is exploited in the number dimension.

The discussions of the featured modulation options for OFDM-based waveform will be presented in the following subsections.

5.4.1 Conventional and differential digital modulations for OFDM-based waveform The subcarriers of the conventional OFDM are occupied by conventional constellation symbols [12]. The related main frequency domain block can be built as T x = s(1) s(2) · · · s(N ) ,

(5.2)

where s(k) ∈ S. The block diagram of the conventional OFDM transmitter is presented in Figure 5.2. As noticed from Figure 5.2, the basic OFDM system is efficiently synthesized by FFT that simplifies the system complexity; therefore, the OFDM transmission scheme has been widely employed in the present wireless communication standards and systems. In OFDM-DM transmission scheme, the incoming binary data bits are encoded in the difference between the consecutive subcarriers within the same OFDM symbol [19]. This differential encoding is named as frequency domain differential modulation. Another way of differential encoding called time-domain differential modulation in which the data symbols are conveyed by the difference between the adjacent OFDM symbols over the same OFDM subcarrier [19]. The main difference between the OFDM-DM and its counterpart, i.e., conventional OFDM, in terms of their block diagrams is the type of the used modulators

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Flexible and cognitive radio access technologies for 5G and beyond

Input data

Conventional modulation

S/P

1

1

2

2

3

N

IFFT

3

P/S

N

Figure 5.2 The block diagram of the conventional OFDM transmitter. © 2019 IEEE. Reproduced, with permission, from [10].

and demodulators. Particularly, differential and conventional modulator/demodulator blocks are used in OFDM-DM and conventional OFDM, respectively. The OFDMDM scheme enjoys a low-complex detection alongside avoiding channel estimation and equalization. However, a higher signal-to-noise ratio (SNR) is needed in OFDM-DM to have a similar transmission rate as in the plain OFDM.

5.4.2 Multi-dimensional modulation options for OFDM-based waveform In the last decade, various transmission mechanisms have been seriously considered by exploiting specific parameters of a given communication system to enhance the data rate at neither increased transmission power nor bandwidth cost. In the literature, novel modulation schemes introduce new constellation diagrams besides the conventional ones with a basic motivation for conveying additional information bits. Several modulation options exploit the third dimension alongside the 2-D signal plan with the major target of sending additional information. The exploited third dimension is determined depending on the application, its requirements, and capabilities. Examples of these featured modulation schemes are SM-OFDM, OFDM-IM, OFDM-SNM, and OFDM-PSM. Figure 5.3 shows the generic block diagram for the transmitter of the multi-dimensional modulation option for the OFDM-based waveform. The function of the frequency domain OFDM block builder is basically forming x based on the used OFDM-based modulation option. Some examples of these options and their x are shown in Table 5.1. The detailed descriptions of these multidimensional modulation options are presented in the following sections.

Generalized and flexible modulation options

Input data

S/P

Frequencydomain OFDM block ( ) builder

1

1

2

2

3

IFFT

N

3

151

P/S

N

Figure 5.3 The generic transmitter of the multidimensional modulation option for OFDM-based waveform

p1 p

p2

m bits

Mediumbased index selection M-ary modulation

Secondary subblock creator 1

Primary OFDM block creator

Bit splitter

p1 p

p2

Mediumbased index selection M-ary modulation

Conventional OFDM modulator (IFFT+CP+DAC)

Secondary subblock creator G

Figure 5.4 The generic transmitter of the index-based modulation options for OFDM-based waveform

5.5 Index-based modulation options Recently, index-based modulation schemes have witnessed considerable research interest in literature. In these schemes, extra data is sent by utilizing a third dimension called the index of the medium(s). The generic transmitter for index-based modulation schemes for the OFDM-based waveform is shown in Figure 5.4. The medium-based

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Flexible and cognitive radio access technologies for 5G and beyond

index selection block depends on the used medium for transmitting additional data bits such as antennas in the SM-OFDM. For further explanation of transmitter of the index-based OFDM family, its common members are illustrated as follows.

5.5.1 SM-OFDM scheme SM is a novel transmission scheme that exploits the indices of building blocks called transmit antennas in the spatial domain to embed extra information bits [20]. The featured SM options are explained in Chapter 9. These schemes will not be covered in Chapter 5 as they fall beyond the main scope of this chapter. In SM, groups of Mc +Ma bits are taken and Mc bits mapped to an information symbol, and Ma bits to the index of specific transmit antenna in such a way that the same data symbol sent by different transmitting antennas conveys extra information, where Ma ≤ log2 (NT ) and NT is the number of transmitting antennas [21]. The application of SM to OFDM system can be represented as mapping each OFDM subcarrier to single transmit antenna, i.e., the corresponding transmit antenna would transmit power on a specific subcarrier only and keep the remaining transmit antennas idle at each transmission instant [4]. The SM-OFDM block structure in frequency domain would be different for different transmit antennas. The block diagram of the SM-OFDM transmitter is depicted

1

2

Q

Spatial modulation

Conventional OFDM modulator (IFFT + CP + DAC)

Conventional OFDM modulator (IFFT + CP + DAC)

W

NT

Conventional OFDM modulator (IFFT + CP + DAC)

Figure 5.5 The block diagram of the SM-OFDM transmitter. © 2019 IEEE. Reproduced, with permission, from [10].

Generalized and flexible modulation options

153

in Figure 5.5 [4,22]. Given a specific number of bits per symbol per subcarrier (u), the SM block maps the binary matrix Q of size u × N to the matrix W of size NT × N . The data to be sent in one subcarrier can be represented by the column vectors of Q. Each column vector of W corresponds to the symbol carried by a subcarrier and contains a non-zero element at the position of the mapped transmit antenna number. Afterward, classical OFDM modulators are employed to modulate the NT row vectors of W. Figure 5.6 exhibits the 3-D signal plane of SM-OFDM where only a single antenna is active out of NT transmit antennas for each subcarrier. A data symbol can be represented as a colored cube along the space axis as shown in Figure 5.6. Example: In Table 5.2, a unique SM-OFDM mapper is displayed between Q and W for u = 2, N = 4, and BPSK. The vector q represents the possible vector of the matrix Q, and w = [w1 w2 ] vector contains the mapped BPSK symbols (w1 and w2 )

Frequency

Time

1 2 4

3

NT Space

Figure 5.6 The 3-D signal representation of the SM-OFDM transmission scheme. © 2019 IEEE. Reproduced, with permission, from [10].

Table 5.2 Antenna-based index selector with u = 2, N = 4, and BPSK q

w

[0 0] [0 1] [1 0] [1 1]

[−1 0] [1 0] [0 −1] [0 1]

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Flexible and cognitive radio access technologies for 5G and beyond

that correspond to the first and second transmit antennas, respectively. Moreover, it is not possible to map a single q pattern to two or more transmit antennas.

5.5.2 OFDM-IM scheme With the inspiration of SM-OFDM, OFDM-IM has been proposed without the need for multiple antennas at the transmitter. Concerning Figure 5.4, the medium exploited in the OFDM-IM, as an index-based modulation scheme, is the active subcarriers. It should be noted that the featured frequency domain-based IM options are explained in Chapter 6. These schemes will not be covered in Chapter 5 as they fall beyond the main scope of this chapter. The OFDM-IM scheme [5,23] divides the OFDM block into G subblocks with a specific number of available subcarriers. The main idea in OFDM-IM is selecting A active subcarriers out of all available subcarriers and then sending classical constellation symbols over the selected ones. The incoming bit stream (m) are split among G subblocks, and p bits enter each OFDM-IM subblock (g) of b available subcarriers and then they are split into two portions. The first portion contains p1 bits, which controls the selection of active subcarriers indices of length a in g = 1, . . . , G. Then, the chosen active subcarriers would be sorted in ascending order as ig = {ig,1 , ig,2 , . . . , ig,a },

(5.3)

where ig,γ ∈ [1, . . . , b] for γ = 1, . . . , a. The second portion of the input bits to the OFDM-IM subblock carries a conventional symbols (p2 = a log2 (M ) bits). The resultant vector corresponding to the active subcarriers of the g subblock carrying the classical QAM symbols can be represented as T sg = sg (1) sg (2) · · · sg (k) , (5.4) where sg (γ ) ∈ S, S is the transmitted QAM symbols over ig,γ . After that, the selected active subcarriers (ig ) and their corresponding data vector (sg ) are concatenated for all G subblocks to build the main frequency domain OFDM-IM block. Example:Table 5.3 shows an example of a unique mapping between the incoming bits and their corresponding subcarrier activation pattern (SAP) for activation ratio of a/b = 2/4 = 1/2. The part of the bit stream that controls the subblock activation is p1 , which equals p1 = log2 (b) = 2 bits, and the other part of the input bit stream Table 5.3 Active subcarrier-based index selector with p1 = 2 bits and b = 4 Incoming bit stream

SAP

[0 0] [0 1] [1 0] [1 1]

[1 1 0 0] [0 1 1 0] [0 0 1 1] [1 0 0 1]

Generalized and flexible modulation options

155

is named as modulation symbol bits: p2 = a log2 (M ) = 2 log2 (M ). As seen from Table 5.3, based on the incoming bit pattern, the possible sets of active indices could be determined as {1, 2}, {2, 3}, {3, 4}, and {1, 4}. It is noticed that a fixed activation ratio of 2/4 is assumed in this example.

5.6 Number-based modulation options In the number-based modulation options for OFDM-based waveform, additional data could be transmitted by utilizing a novel third dimension called the number of transmitting entity (or entities). The generic transmitter of the number-based modulation options for OFDM-based waveform is shown in Figure 5.7. The medium-based number selection block depends on the employed medium in transmitting extra information like active subcarriers as in OFDM-SNM proposed in [7]. OFDM-SNM employs index-independent SAP instead of the index-dependent pattern as in OFDM-IM. The number of active subcarriers of OFDM-SNM is specified based on the input data sequence. The activated subcarriers in OFDM-SNM are not necessarily adjacent to each other. The novel OFDM-SNM scheme utilizes a new dimension called the number of active subcarriers. Thus, the OFDM-SNM scheme conveys information both by constellation symbols and through the number of active subcarriers conveying them. A unique information mapping is observed in the OFDM-SNM scheme. This number-based modulation scheme differs from OFDM-IM in that it is based on the variable number of active subcarrier (a) in each subblock of length b in the OFDMSNM system, whilst it is fixed for all OFDM-IM subblocks. In the OFDM-SNM

p1 p

p2

m bits

Mediumbased number selection

M-ary modulation

Secondary subblock creator 1

Primary OFDM block creator

Bit splitter

p1 p

p2

Mediumbased number selection M-ary modulation

Conventional

OFDM modulator (IFFT+CP+DAC)

Secondary subblock creator G

Figure 5.7 The generic transmitter of the number-based modulation options for OFDM-based waveform

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Flexible and cognitive radio access technologies for 5G and beyond

transmission scheme, there is index-independent number of active subcarriers a out of b available subcarriers for each OFDM-SNM subblock based on its corresponding p1 = log2 (b) bits. The p2 = a log2 (M ) bits is variable depending on the number of active subcarriers in each subblock, which are mapped onto M -ary signal constellation transmitted over the active subcarriers. Figure 5.8 presents an example of a 3-D signal representation of OFDM-SNM where the number of adjacent active subcarriers varies in each OFDM-SNM subblock based on the input data bits. In Figure 5.8, four possibilities of SAP assignment correspond to OFDM-SNM subblock with a length of four subcarriers. The complex

Frequency Sublock SSubcarrier Su index index in N G

N–1 N–2 N–3

12 11

3

10 9 8 7 2 6 5 4 3 1

2 1

Space

Figure 5.8 3-D signal plane of the OFDM-SNM scheme. © 2019 IEEE. Reproduced, with permission, from [10].

Time

Generalized and flexible modulation options

157

Table 5.4 Active subcarrier-based number selector with p1 = 2 bits and b = 4 Incoming bit stream

SAP

[0 0] [0 1] [1 0] [1 1]

[1 0 0 0] [1 1 0 0] [1 1 1 0] [1 1 1 1]

QAM symbols would be loaded on the subblock width, i.e., a number of active subcarriers, as shown in the colored cubes along the frequency axis. Example: Table 5.4 presents an example of a unique mapping between the transmitter input bits and their corresponding SAP for b = 4 and a ∈ [1, 2, 3, 4]. The part of the bit stream that controls the subblock activation is p1 which equals p1 = log2 (b) = 2 bits, and the other part of the incoming stream to the subblock is called modulation symbol bits that is represented by p2 = a log2 (M ). As seen from Table 5.4, based on the input information bits to the corresponding subblock, the SAP is first loaded with single active subcarrier and then the SAP is loaded with the ones until the subblock is filled. The possible sets of active subcarriers in this example are {1}, {1, 2}, {1, 2, 3}, and {1, 2, 3, 4}. An enhanced scheme of OFDM-SNM is proposed in [24], which exploits the flexibility offered by the original OFDM-SNM scheme by placing subcarriers to increase a coding gain in the high SNR region by forming an adaptive modulation technique. More specifically, the instantaneous channel state information (CSI) adaption is employed by dynamically mapping the incoming information bits to subcarriers with high channel power gains.

5.7 Shape-based modulation options The shape of the transmitting entity (or entities) could be exploited to add another degree of freedom to enhance SE of a given system. One example of these shape-based OFDM modulation schemes is the OFDM-PSM, where data symbols can be carried onto time–frequency-shifted versions of a transmit pulse. Several types of pulse can be found in the literature, particularly some of them are commonly used [25]. Different data symbols are carried by different orthogonal, fully overlapping pulses in OFDMPSM [8]. These pulses are superimposed together within the same time–frequency region to improve the SE while keeping a comparable reliability performance to that of the conventional OFDM. As seen in Figure 5.9, four superimposed HG pulses cover approximately twice the time–frequency region as compared to two adjacent Gaussian pulses.

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Flexible and cognitive radio access technologies for 5G and beyond

–60 -60

–40

–20

00

20 20

40 40

60 60

Figure 5.9 Conventional Gaussian transmission (upper part of the figure) versus pulse superposition modulation using HG pulses (lower part of the figure). © 2019 IEEE. Reproduced, with permission, from [10]. Hence, OFDM-PSM with HG pulses achieves improved SE over the schemes that utilize only single Gaussian transmit pulses. On the other hand, OFDM-PSM has fewer sidelobes due to the usage of localized HG pulses instead of the nonlocalized Sinc pulses as in classical OFDM. Another integration of the pulse superposition approach

Generalized and flexible modulation options

159

is its integration with generalized frequency division multiplexing (GFDM) [26]. This integration results in almost 2.4 much higher than the integration of Gaussian pulses with GFDM [27].

5.8 Performance evaluation and comparison of modulation options in practical conditions The performance metrics used in the assessment of the featured modulation schemes for OFDM-based waveform are SE, reliability, PE, OOB leakage, and computational complexity. Monte Carlo simulations are conducted to evaluate their performances and the simulation parameters used can be shown in Table 5.5. Point-to-point transmission with a single user is considered to have a fair comparison between the featured modulation schemes. It is assumed that the transmitter and receiver have a single antenna. The multi-antenna transmission scheme discussed in Section 5.5.1, i.e., SMOFDM would be analyzed with its counterparts, including the famous ones such as Vertical Bell Labs Layered Space-Time (V-BLAST) [28] and Alamouti-Coded [29] OFDM systems. A Rayleigh fading channel is used in the conducted simulations, and the CSI is available at the receiver. SNR is defined as ρ = Eb /No,T , where Eb is the average transmitted energy per bit and No,T is the time-domain noise variance. The SNR region in the considered simulations is between 0 and 30 dB for practical purposes; however, similar realization trends have been noticed for other possible SNR values.

5.8.1 Spectral efficiency The SE is important for data rate requirements, user communication, and traffic density. The SE (η) in (bits/s/Hz) of the featured modulation options for OFDM-based waveform can be formulated as ηPlain OFDM =

N log2 (M ) , N + NCP

(5.5)

Table 5.5 Simulation parameters Modulation type

BPSK

FFT size (N ) CP guard interval (samples) Number of subblocks in each OFDM symbol Number of available subcarriers in each subblock Multipath channel delay samples locations Multipath channel tap power profile (dBm)

64 8 16 4 [0 3 5 6 8] [0 −8 −17 −21 −25]

160

Flexible and cognitive radio access technologies for 5G and beyond N log2 (M a ) + log2 ba ηOFDM-IM = , (N + NCP )b

G ηOFDM-SNM =

g=1

(5.6)

log2 (b) + a(g) log2 (M ) N + NCP

,

(5.7)

where NCP is the cyclic prefix (CP) or guard length and a represents the number of activated subcarriers in an OFDM-IM subblock with a length of b; a(g) is the number of turned on subcarriers in an OFDM-SNM subblock with a length of b and G represents the number of subblocks that constitute the OFDM-SNM block. As can be seen in Figure 5.10, the relative loss in throughput is higher in OFDMDM compared to classical OFDM [19]. a(g) varies in each subblock of the OFDMSNM scheme, while it is fixed for all OFDM-IM subblocks. The SE improvement of OFDM-SNM over OFDM-IM holds just when subblock length is low as implemented in the considered deployment option in the simulations. However, the SE improvement does not hold anymore for more general cases. As M increases, it is less likely to have enhanced SE of OFDM-SNM as compared to the plain OFDM [24]. In addition to that, the average SE of OFDM-SNM could be higher than that of OFDM-IM when [24] a≤

b+1 . 2

(5.8)

1.15 1.1

Throughput (bps/Hz)

1.05 1 0.95 OFDM-IM

0.9

OFDM-SNM OFDM-DM

0.85

Conventional OFDM

0.8 0.75 0.7 0

5

10

15 Eb/No,T (dB)

20

25

30

Figure 5.10 Throughput of the featured OFDM-based modulation options. © 2019 IEEE. Reproduced, with permission, from [10].

Generalized and flexible modulation options The selected M based on (5.8) could be formulated as [24] b (1/(a−((b+1)/2))) M ≥ b/ . a

161

(5.9)

As the number of transmit antennas increases, the SE of SM-OFDM increases in a logarithmic way as ηSM-OFDM =

N log2 (NT ) + log2 (M ) . N + NCP

(5.10)

The SE of OFDM-PSM with HG pulses is superior to the transmission scheme that uses only Gaussian pulses as the transmit pulse. Figure 5.10 shows that the achievable rate of the OFDM-SNM scheme outperforms its counterparts for the given simulation parameters in Table 5.5. Furthermore, throughput performances of the OFDM-DM and OFDM-IM converge to that of classical OFDM at high SNR values. Moreover, the plain OFDM scheme outperforms OFDM-DM by almost 2.9 dB [19]. It is worthy to mention that changing some parameters, such as subblock size and activation ratio, in a given modulation scheme results in achieving diverse transmission rates.

5.8.2 Reliability The bit error rate (BER) performance of the featured OFDM-based modulation schemes is depicted in Figure 5.11, in which OFDM-SNM and OFDM-IM have

100

OFDM-IM OFDM-SNM OFDM-DM Conventional OFDM

BER

10–1

10–2

10–3

10–4 0

5

10

15 Eb/No,T (dB)

20

25

30

Figure 5.11 BER of the featured OFDM-based modulation options. © 2019 IEEE. Reproduced, with permission, from [10].

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Flexible and cognitive radio access technologies for 5G and beyond

almost similar reliability performance that is superior to the classical OFDM in the high SNR region. The OFDM-DM transmission scheme has the worst BER performance at highSNR values. Furthermore, OFDM-PSM has almost the same reliability performance to conventional OFDM [27]. The SM-OFDM scheme has superior reliability performance as compared to the popular V-BLAST and Alamouti-coded OFDM systems at low SE. Even though, at high SE, its reliability performance would rely on a trade-off between the spatial constellation size (NT × NR ) and signal constellation size (M ) [22].

5.8.3 PAPR and power efficiency The PE analysis of modulation schemes is critical to be considered in the system design. PAPR of a given waveform is defined as the ratio of maximum power to average power. High PAPR is a common drawback in the multi-carrier waveforms. This high PAPR performance of the OFDM transmission scheme causes nonlinearity problems as the transmitted signal passed through the high power amplifier (HPA). Thus, an efficiency reduction would be expected at the HPA. This phenomenon happens due to having a constructive combination between the modulated subcarriers with independent phases. The number of available subcarriers (N ) and their corresponding pulse shapes are the major factors of PAPR performance in the OFDM-based waveforms. The total transmitted power (Pt ) is distributed fairly among subcarriers with (Pt /N ) average power allocated for each subcarrier. As demonstrated in [10], the featured modulation options, including OFDM-SNM, OFDM-IM [30], OFDM-DM, and traditional OFDM, have almost the same high PAPR performance. It is assumed that OFDM symbols are uncorrelated to each other within each block. Although the number of active subcarriers in some transmission schemes such as OFDM-SNM and OFDM-IM is less than that used in the classical OFDM, their PAPR performances remain high and comparable to the plain OFDM. This is due to the sparsity nature of the activated subcarriers in OFDM-SNM or OFDM-IM. The PAPR of OFDM-PSM is also high as in the conventional OFDM, due to the superimposed pulses within the same time–frequency region that results in a significant increase of the peak power of the transmitted signal. For a large number of subcarriers, the SM-OFDM attains similar PAPR performance compared to the V-BLAST OFDM system [31]. Besides the PAPR as one way to assess the PE of a given modulation scheme, there could be another way to evaluate the PE by considering the inactive subcarriers. A reduction in power consumption is expected in some modulation schemes, such as OFDM-SNM and OFDM-IM, which have some inactive subcarriers of zero power whereas its active subcarriers only carry data symbols. The average power per active subcarrier in OFDM-IM is Pt /aN . Observing a active subcarriers in the OFDM-SNM subblock of b available subcarriers follows a binomial distribution of Nc as a random variable b a P(Nc = a) = p (1 − p)b−a . (5.11) a

Generalized and flexible modulation options

163

It is assumed that the allocated power to the OFDM-SNM block (Pt ) is equally distributed to its subblocks. Therefore, a fair comparison between OFDM-SNM and its competitive schemes is possible. The power consumed in OFDM-SNM could be formulated as [7] Pt a(g) P(Nc = a(g)), G b g=1 G

Pc =

(5.12)

where a(g) is the number of activated subcarriers in the gth subblock. Thus, the average power assigned for each subcarrier is Pc /N . The SM-OFDM scheme is considered as an energy-efficient transmission scheme as the power consumed at its transmitter does not depend on the number of transmit antennas [32]. Figure 5.12 shows the PE that can be achieved due to not sending on all OFDM subcarriers of some featured OFDM modulation schemes such as OFDM-IM, OFDM-SNM, and classical OFDM under BPSK and quadrature phase shift keying (QPSK). For a fair comparison between the aforementioned schemes, an average of unit transmit power per subcarrier is assumed. In Figure 5.12, this PE value is calculated as Nb Peff = , (5.13) Ka P S where Ka represents the average number of active subcarriers, PS is power assigned for each subcarrier that is assumed to be unity, Nb is the total number of bits per block, which is computed as Nb = Ka Ns Na ,

(5.14)

where Ns is the number of bits corresponding to M -ary complex symbols employed on each active subcarrier, Na is the number of extra bits transmitted by a transmitting entity (or entities) such as index modulus observed in OFDM-IM or number modulus in OFDM-SNM. It is observed from Figure 5.12 that the OFDM-IM obtains 3

Power efficiency

2.5

OFDM-IM OFDM-SNM Conventional OFDM

2 1.5 1 0.5

BPSK

QPSK

Figure 5.12 PE of OFDM-IM, OFDM-SNM, and conventional OFDM

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Flexible and cognitive radio access technologies for 5G and beyond

enhanced PE over OFDM-SNM and plain OFDM due to a less average number of active subcarriers in OFDM-IM compared to its counterparts.

5.8.4 Out-of-band leakage Frequency localization is advantageous for efficient spectrum utilization and multiplexing of various 5G services on single carrier using diverse waveform numerologies. In OFDM, sidelobes are observed due to the multiple subcarriers of Sinc-shaped. Particularly, larger sidelobes lead to high OOB leakage, which is one of the major setbacks in the OFDM system [33]. Therefore, it is required to have new modulation options with considerable low levels of OOB leakage. As proved in [10], the OOBE leakage is almost the same for the featured modulation schemes, including OFDM-SNM, OFDM-IM [34], OFDM-DM, and classical OFDM. Particularly, the sparse distribution of the activated subcarriers in the OFDMSNM and OFDM-IM transmission blocks causes this high OOBE leakage. Moreover, the OOB leakage of SM-OFDM reaches a minimal level due to activating single transmitting antenna once at a time, which considerably lowers the interference between the transmitting antennas. Exploiting localized pulses such as HG pulses in OFDM-PSM contributes to having less OOBE leakage as compared to the non-localized pulses used in the plain OFDM.

5.8.5 Computational complexity The baseband complexity is critical at the user equipment, particularly at the receiver side. Also, a low baseband complexity speeds the processing and allows low-latency applications. The assessment tool used in the complexity analysis is the required number of complex operations in each subcarrier. Channel estimation or equalization operations are not considered in the analysis. Moreover, perfect synchronization is assumed in the analyzed transmission schemes. The classical OFDM is considered as an efficient scheme in terms of computational complexity due to the employed IFFT and FFT process to the OFDM system. The conventional OFDM and OFDMDM are considered as low-complex schemes and their computational complexities rely on the employed modulation order. Moreover, the OFDM-IM scheme with nearoptimal log-likelihood ratio detector has comparable complexity to the conventional and differential transmission schemes for OFDM waveform. On the other hand, the complexity of the OFDM-SNM scheme can be seen in its subcarrier activation process in each subblock as well as the used modulation order. The evaluation of the computational complexity of the OFDM-PSM is based on the number of polyphase components (R) of the filter, length of filter (U ), HG function order (D), and the used modulation order (M ) [27]. As the modulation order, number of transmitting and receiving antennas (NT , NR ) increase, the SM-OFDM complexity increases [35]. The SM-OFDM transmission could be handled by only a single radio frequency (RF) chain and a switch since a single transmit antenna is activated at each time instant. Moreover, the number of mapped bits in the SM-OFDM scheme is limited in the case of using a small number of antennas in a wireless device.

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5.9 Applications of the featured modulation options for 5G and beyond networks New services, scenarios, and applications alongside a lot of challenging requirements would appear in the next communication networks. Single radio technology almost cannot satisfy all these requirements at the same time especially with diverse future services, including massive machine type communications (mMTC), enhanced mobile broadband, and ultra-reliable low latency communication (URLLC). In [1,36], the multi-numerology OFDM concept is introduced to provide a more flexible solution in satisfying various requirements for different services in future communication networks. The modulation option for a given waveform is expected to have a considerable impact on the waveform capability to meet the diverse services and requirements in the next-generation wireless networks. The featured modulation schemes have varied performances for different metrics and criteria as presented in Section 5.8, where different reliability and throughput performances are achieved.† With setting-specific parameters for a given modulation scheme, the requirements of a given 5G service could be met. Tables 5.6 and 5.7 show a qualitative summary of the performance of the featured modulation schemes for OFDM-based waveform. Thus, the possible applications of these featured modulation options for the future networks can be predicted. The main criteria to assess the effectiveness of a modulation option are its reliability, SE, PE, OOBE, and their complexity performances. The possible applications of the OFDM-SNM scheme are the ones that require relatively high transmission rate with low M , i.e., M = 2, to offer reduced BER values and with a small length of the subblock, i.e., four subcarriers, to attain lowcomplex detector. Moreover, the OFDM-SNM inherent features of floating the active subcarriers could be exploited for the applications where robustness toward adjacent channel interference is preferable, especially in 5G multi-numerology design. The flexible, low-complex OFDM-IM scheme with an energy detector could be a potential candidate for the Internet of Things (IoT) applications where limited power of devices as well as robustness against asynchronous impairments are the main features of such applications. Furthermore, employing a low-complex compressed sensing detector in OFDM-IM alongside low activation ratio could be exploited for URLLC type of services due to low latency with high reliability performances offered by such structure. In the next-generation wireless networks, a robust receiver toward unknown interference is needed. The number-based and index-based modulation options tailored to

† It is worthy to mention that the simulation setup example presented in this chapter is provided to demonstrate a simple, basic comparison between the featured modulation options for OFDM-based waveform. However, it should be emphasized that the differences in the reliability and throughput performances can be far greater as they are heavily dependent on many other parameters such as the subblock size, activation ratio, and M . For example, a comprehensive, detailed study on the huge range of different throughput and reliability values that OFDM-IM is capable of obtaining can be found in [23], where the performance comparisons of both low and high SE scenarios are presented.

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Table 5.6 Assessment of the featured modulation options in terms of reliability, SE, and OOB leakage OFDMbased modulation scheme

Reliability

Spectral efficiency

OOB leakage

Conventional High at low SNR and OFDM [12] low at high SNR

(N log2 (M ))/(N + NCP )

High due to the rectangular transmit pulse

OFDM-DM [13]

Low at low and high SNR

Relative loss in throughput is higher compared to plain OFDM

High

SM-OFDM [4]

High at low SE, depends on a trade-off between NT × NR and M at high SE

(N log2 (NT ) + log2 (M ))/ (N + NCP )

Low due to significantly reduction in the interference between the transmit antennas

OFDM-IM [5]

Low at low SNR and high at high SNR

OFDMSNM [7]

Low at low SNR and high at high SNR

OFDMPSM [8]

High at low SNR and low at high SNR

N log2 (M a ) + log2 ba / (N + NCP )b)

High due to the sparsity nature of the activated subcarriers

log2 (b) + a(g) log2 (M ) / High due to the sparsity nature of (N + NCP ) the activated subcarriers High and depends on the number Low due to using of superimposed HG pulses localized HG pulses G g=1

the OFDM waveform are promising candidates against unknown interference especially with employing a robust receiver, including a low-complex, threshold-based detector. Utilizing the inactive subcarriers in the index-based and number-based modulation schemes, such as OFDM-SNM and OFDM-IM, could be helpful for 5G use cases where simpler ICI mitigation and low PAPR are crucial. The SM-OFDM scheme is suitable for mMTC use cases due to its enhanced PE and reliability along with flexibility. Due to the preferable OOB leakage performance of OFDM-PSM, it could be applied in applications where multiuser interference mitigation is favorable.

5.10 Other potential flexible modulation options for OFDM-based waveforms Due to flexibility offered by the OFDM-based waveforms, there is a vital need for additional flexible modulation options for OFDM-based waveforms to be proposed. It is reasonable and advantageous to incorporate resources across various signal domains

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Table 5.7 Assessment of the featured modulation options in terms of PE and computational complexity OFDMbased modulation scheme

PE in terms of PAPR

PE in terms of transmitted power

Computational complexity

Conventional OFDM [12]

High due to the constructive combination of subcarriers with independent phases

High due to transmitting power on all subcarriers

O(M )

OFDM-DM [13]

High

High due to transmitting power on all subcarriers

O(M )

SM-OFDM [4]

High for a large number of subcarriers

Low due to transmitting power only to single transmit antenna at a time

O(NT , NR , M )

OFDM-IM [5]

High due to sparse distribution of active subcarriers

Low due to transmitting power to fixed number of active subcarriers in each subblock

O(M )

OFDM-SNM [7]

High due to sparse distribution of active subcarriers

Medium and depends on the varied number of active subcarriers in each subblock

O(b, a, M )

OFDM-PSM [8]

High due to superimposing pulses together within the same time–frequency region

High and depends on the number of superimposed HG pulses

O((D + 1), R, U , M )

to shape a multi-dimension entity for the implementation of the presented OFDM modulation options. One example of these flexible modulation options is OFDM with subcarrier power modulation (OFDM-SPM) in which the power of subcarriers is exploited as a novel dimension to carry additional information bits beside the conventional PSK/QAM symbols. As compared to conventional OFDM, the novel OFDM-SPM scheme doubles the SE, and it uses only half the number of subcarriers required by OFDM with BPSK, and thus power is saved. The flexibility of this powerbased OFDM scheme is possible by introducing more degrees of freedom for the saved power, in which it could be used for merely power saving or power reallocation, which makes the OFDM-SPM transmission scheme suitable for low complexity and power-efficient applications [37]. In the conventional transmission scheme, different QAM modulation orders are selected and applied to different subcarriers based on their corresponding subchannels’ quality and/or the requirements of the service or application being used at the receiver side (i.e., quality-of-service (QoS)-based adaptation). Particularly, given service with a certain, targeted BER requirement, the modulation orders are adaptively

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selected to meet the targeted BER while maximizing the transmission SE. Besides the conventional adaptive QAM modulation, there is also the concept of joint adaptive subcarrier switching and adaptive modulation [38]. More specifically, three approaches can be used in this case: (1) OFDM with adaptive IM and fixed constellation modulation, which can be used to enhance secrecy and SE; (2) OFDM with joint adaptive IM and adaptive constellation modulation, which can be used to further enhance the secrecy and SE; (3) OFDM with variable IM and variable constellation modulation for QoS based communication to improve SE. In particular, the first two approaches are based on channel adaptation of subcarrier activation ratios and constellation modulation orders of subblocks in OFDM-IM by utilizing channel reciprocity concept in time division duplex mode, whereas the third approach is based on QoS-based adaptation. A small degradation in the BER performance is observed while examining the proposed algorithms against imperfect channel estimation [38]. The presented modulation options are applicable not only to plain OFDM, but also for various OFDM variants [39]. The most common features of these OFDM variants are their application of windowing and/or filtering operation in either frequency or time domain. Examples of these OFDM-like structure are filtered-OFDM (f-OFDM) [40], which employs windowing or filtering operation in time domain, filter bank multicarrier (FBMC) [41] that filters the data in frequency domain at a subcarrier level, universal filtered multicarrier (UFMC) [42] that performs filtering in frequency domain at the resource block level, and GFDM that applies a circular convolution to directly apply filtering on a time–frequency block [26]. The detailed explanation of the OFDM-based waveforms can be found in Section 2.3. Some examples of proposed modulation options for these OFDM variants are shown in Table 5.8.

Table 5.8 Modulation options for OFDM variants OFDM variant

Modulation option for OFDM variant

Contributions

FBMC

Circular convolution FBMC-IM [43]

GFDM

GFDM with pulse superposition modulation [27] Universal filtered OFDM with filter shift keying [44]

Circular convolution FBMC-index modulation provides more OOBE suppression as compared to conventional OFDM The acquired bandwidth efficiency of the superimposed GFDM waveform is almost 2.4 much higher than GFDM with Gaussian pulses The filter realizations carry information through the index modulation. Universal filtered OFDM with filter shift keying provides significant throughput and error performance improvements over UFMC In spatial modulation f-OFDM, each subcarrier within the subband is mapped to one of the transmitting antennas. Spatial modulation f-OFDM provides higher SE as a natural result of both SM and f-OFDM benefits

UFMC

f-OFDM

Spatial modulation f-OFDM [45]

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5.11 Futuristic modulation options for beyond 5G The presented modulation options for OFDM and its variants are not sufficient to meet diverse services in 5G and beyond networks. Further modulation schemes needed to be explored for different waveforms, not limited to OFDM and its variants, in either onedimensional or multidimensional depending on the application, their requirements and capabilities. For example, the link adaptation schemes adjust over-the-air transmission parameters based on the radio channel change for both downlink and uplink. The CSI measurements for link adaptation include some parameters such as channel quality indicator (CQI), which are reported back to the transmitter. However, this conventional link adaptation hardly meets the diverse 5G and beyond networks requirements, as shown in Figure 5.13. Hence, there is a need for adaptive modulation options for a given waveform in 5G and beyond networks, as presented in Figure 5.14. It is worthy to note that there is no best modulation format, and the adopted modulation depends on the system requirements, channel model and transceiver constraints, etc. Unconventional transmission methods may have remarkable potential and impact to shape beyond 5G due to their inherently available advantages over conventional systems. For example, IM schemes have been considered as alternative solutions for 5G and beyond wireless networks. One IM scheme that received a significant amount of research, due to its promising performance, is the transmitter SM as described in Section 5.5.1. One of the variants of SM systems is called receive SM (RSM) [46], where the transmitted signals from all transmit antennas are precoded, such that only a single antenna receives the modulated symbol, while the received signals at all other receive antennas include only noise. RSM is shown to require less computational complexity, measured by the number of the required real multipliers at the receiver, as compared to the transmitter SM. Further details about the transmitter and receiver SM and many other SM variants can be found in Chapter 9. Another potential IM-based candidate for beyond 5G, which attracts recent research attention, is MBM [18]. MBM offers multiple-input multiple-output (MIMO) benefits, and it introduces a completely new dimension in the channel states domain for conveying information by altering the far-field radiation pattern of reconfigurable antennas. In other words, MBM converts static Rayleigh fading to additive

Adaptive/ flexible MQAM modulation

CQI

Tx waveform

Link communication rate adaptation

Channel

Rx waveform

CSI

Figure 5.13 Block diagram of the conventional link adaptation

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Flexible and cognitive radio access technologies for 5G and beyond Index-based modulation family

Number-based modulation family

Adaptive/flexible modulation options selector

Shape-based modulation family

Tx waveform

….

Channel

Rx waveform

Adaptation control is based on: 1) PAPR. 2) OOBE. 3) Spectral efficiency. 4) Robustness to fading and interference. 5) Compatibility to asynchronous transmission. 6) MIMO compatibility. And many other key performance indicators…

Figure 5.14 Block diagram of the adaptive modulation options selector

white Gaussian noise by performing the modulation of the wireless channel itself in a sense so inherent diversity is supported by MBM. Further details about MBM can be found in Chapter 10. One interesting direction for designing a modulation scheme is a proper combining of multiple dimensions, such as the proposed scheme in [47] entitled “OFDM with hybrid number and index modulation,” where the number and index dimensions are jointly exploited to introduce additional degrees of freedom in the modulation design. A novel modulation technique called orthogonal time frequency and space (OTFS) is proposed to address the challenges of 5G [48]. In OTFS, the information QAM symbols are multiplexed over localized pulses in the delay-Doppler representation. OTFS provides high SE and reliability under diverse channel conditions. OTFS enables significant SE advantages in high order MIMO under general channel conditions over traditional modulation schemes. OTFS is ideal for communication under mobility conditions due to its diversity gain.

5.12 Conclusion In this chapter, the relation between the modulation and waveform is discussed. The flexibility perspectives in the modulation design are also illustrated. Prevalent modulation options for OFDM waveform are classified, studied, analyzed, categorized,

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and compared in terms of their reliability, SE, PAPR, PE, OOB leakage, and their computational complexity performances. Furthermore, the connection between these transmission schemes and the requirements of future wireless networks is provided. Besides the presented modulation schemes for the basic OFDM waveform, some other flexible modulation options for OFDM variants are given. Finally, envision for futuristic modulation options for beyond 5G is presented.

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Chapter 6

Index modulation-based flexible waveform design Seda Tusha1 , Armed Tusha1 , Ertu˘grul Ba¸sar2 , and Hüseyin Arslan1,3

The service limitations of conventional orthogonal frequency division multiplexing (OFDM)-based technologies have motivated academia and industry to seek for new solutions in order to support the emerging services and use cases of future wireless networks. In this chapter, promising frequency-domain index modulation (IM) options, i.e., OFDM with IM (OFDM-IM), generalized OFDM with index modulation (OFDM-GIM), dual-mode OFDM (DM-OFDM), OFDM with interleaved subcarrier IM (OFDM-ISIM), are considered as complementary waveforms of classical OFDM. In frequency-domain IM, data information is sent not only via modulated subcarriers but also via proper activation of the subcarriers resulting in higher spectral efficiency (SE) and better error performance compared with OFDM-based schemes. Furthermore, features of OFDM, including intelligent subcarrier selection and adaptive activation ratio, are assessed. Lastly, the flexible utilization of these features is discussed to control channel effects, hardware impairments, asynchronicity, and to serve wide range requirements of fifth generation (5G) and beyond networks.

6.1 Introduction In contrast to the current fourth generation (4G) technology, where high SE is the primary concern, next-generation wireless networks are expected to support a broad range of applications and use cases, which are mainly categorized under enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). Several unique waveform solutions are proposed to meet the demands of the 5G and beyond networks by both academia and industry. Non-orthogonal multiple accessing schemes, millimeter-wave communication, and multiple-input multiple-output (MIMO) signaling are considered as promising technologies to enhance the SE, transmission reliability, and reduce

1

Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Turkey Department of Electrical and Electronics Engineering, Koç University, Sariyer, Istanbul, Turkey 3 Department of Electrical Engineering, University of South Florida, Tampa, USA 2

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communication latency. Due to the need for flexible wireless architectures to address these requirements, the IM concept has gained significant interest from the researchers worldwide. In contrast to classical communications techniques, where all the available radio resources are used for data transmission, only a fraction of the resource entities, i.e., time slots, antennas, or subcarriers, is utilized through IM. Extra information bits are carried by the indices of active resources to compensate for the decrease in SE due to the partial activation of radio resources. In this chapter, the frequency-domain IM scheme, which is widely recognized as OFDM-IM, is presented as a complementary waveform to OFDM, which is the main multicarrier communication technology of 5G standard. Indexing of the subcarriers in the frequency domain is proposed to improve both SE and energy efficiency of the conventional OFDM systems. Moreover, flexible utilization of OFDM-IMbased schemes is discussed considering the requirements of future services and wireless communication challenges, including stringent synchronization, channel and hardware impairments, and high security. The remainder of this chapter is organized as follows. Section 6.2 introduces the frequency-domain IM concept. Section 6.3 provides a review of state-of-the-art OFDM-IM solutions and compares it with classical OFDM systems. Section 6.4 shows the utilization of the inherent features of OFDM-IM in a flexible manner. Discussions and future research directions for the frequency-domain IM concept are provided in Section 6.5. Finally, Section 6.6 concludes the chapter.

6.2 Index modulation in frequency domain: OFDM with index modulation The current OFDM technology utilizes all the available subcarriers for the purpose of efficient resource utilization. In the case of frequency-domain IM, not all the subcarriers transmit M -ary symbols, but the indices of active subcarriers convey additional information bits. Subcarrier IM OFDM (SIM-OFDM) is an initial attempt in the literature that introduces frequency-domain IM [1]. SIM-OFDM divides incoming m data bits into two parts with the same length. On-off keying data bits corresponding to the first part decide the status of N subcarriers considering the number of one and zero bits. If the number of ones is higher than the number of zeros, subcarrier activation is performed based on the ones and vice versa. Therefore, the number of active subcarriers (A) is variable for each OFDM block. The remaining bits are modulated by conventional M -ary with quadrature amplitude modulation (QAM)/phase-shift keying (PSK) and conveyed by activated subcarriers. In SIM-OFDM, there are control subcarriers for carrying the information of majority bit-value (1 or 0) to the receiver. However, it is assumed that this information is always transmitted correctly. To avoid error propagation stemming from the wrong majority bit-value at the receiver, OFDM block is split into G = N /2 subblocks through enhanced SIM-OFDM (ESIMOFDM), and a single-subcarrier (a = 1) per subblock is activated [2]. Hence, the total number of activated subcarriers for each OFDM block is fixed and equals A = aG. Nevertheless, the performances of SIM-OFDM and ESIM-OFDM are not satisfactory,

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177

and their implementations are impractical. Thus, it is important to emphasize that the general concept for frequency-domain IM is first introduced in [3] and called OFDM-IM. A block diagram of an OFDM-IM transmitter is shown in Figure 6.1. OFDM block is equally divided into G subblocks that consist of b = N /G subcarriers, and a out of b subcarriers are selected to transmit M -ary data symbols [3]. It is important to note that IM cannot be directly applied over the OFDM block, since the number of block realizations can get extremely large due to practical N values from 128 to 2,048 [4]. Each OFDM-IM subblock consists of p = m/G bits, which is divided into p1 and p2 bits. The indices of active subcarriers are defined by p1 = log2 (C(b, a)) bit stream, while the remaining p2 = alog2 (M ) bit stream is mapped to the conventional M -ary symbols that are carried by the activated subcarriers. The indices of active subcarriers for gth subblock are defined as follows: ig = [i(1) i(2) · · · i(a)]1×a ,

(6.1)

where i(γ ) ∈ {1, 2, . . . , b} for g = 1, . . . , G. The transmitted symbols by the active subcarriers of the gth subblock are represented as sg = [s(1) s(2) · · · s(a)]1×a ,

(6.2)

where s(γ ) ∈ S , where S is the set of M -ary symbols S = {s0 s1 · · · sM −1 }, and the (b − a) inactive subcarriers are set to zero. Hence, the transmission vector for the gth subblock is xg = [0 s(1) · · · 0 · · · s(γ ) 0]1×b ,

bits

Index selector

bits bits

m bits

Bit splitter

M-ary symbols

. . .

bits

(0)

(0)

OFDM-IM subblock

(

− ) . .

. .

. .

( )

( )

. .

. .

(

− 1) (0)

Block generator

. .

Index selector

bits bits

(6.3)

M-ary symbols

OFDM-IM subblock (

− 1)

(

− 1)

Figure 6.1 Block diagram of OFDM-IM transmitter

(

− 1)

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Flexible and cognitive radio access technologies for 5G and beyond

where the position of s(γ ) is defined by i(γ ). In the case of a = b, xg turns into the classical OFDM signal. OFDM block is generated via the composition of all G subblocks as follows: x = [x1 · · · xg · · · xG ]1×N ,

(6.4)

where xg (γ ) ∈ {0, S }. Therefore, the total number of conveyed bits per OFDM-IM subblock is calculated as p = p1 + p2 = log2 (C(b, a)) + alog2 (M ),

(6.5)

whereas the number of transmitted bits per OFDM block is m = Gp = G log2 (C(b, a)) + alog2 (M ) ,

(6.6)

where . and C(b, a) denote the floor function and binomial coefficient, respectively. Frequency-domain representation of OFDM-IM and OFDM signals are illustrated in Figure 6.2. As seen in this figure, partial subcarrier activation is performed via OFDMIM, and extra information bits are transmitted through the indices of active subcarriers

1

Amplitude

OFDM-IM 0.5

0

–0.5 1

2

3

(a)

4

5

6

7

8

Subcarrier index 1

Amplitude

OFDM 0.5

0

–0.5 1

(b)

2

3

4

5

6

7

8

Subcarrier index

Figure 6.2 Frequency-domain representation of (a) OFDM-IM with a = 3, b = 8 and (b) classical OFDM

Index modulation-based flexible waveform design

179

with ig = [1, 5, 7]. Later, time-domain samples of OFDM block are obtained by inverse fast Fourier transform (IFFT) process as xt (n) =

N −1

x(k)e j2π nk/N , 0 ≤ n ≤ N − 1.

(6.7)

k=0

Before the transmission of xt (n), a cyclic prefix (CP) with length L is appended to the signal to avoid inter-symbol interference due to the time dispersion of multipath channel. After xt (n) leaves the transmitter (Tx), it undergoes an independent and identically distributed multipath Rayleigh fading channel as yt (n) =

L−1

ht (l)xt (n − l) + w(n),

(6.8)

l=0

where L denotes the number of channel taps and path gains ht (l) are Gaussian random variables with CN (0, 1/L). w(n) represents additive white Gaussian noise (AWGN) with zero mean and variance σw2 . After CP removal, fast Fourier transform (FFT) process is performed over the received signal yt (n) as follows: y(k) =

N −1

yt (n)e−j2π nk/N , 0 ≤ k ≤ N − 1.

(6.9)

n=0

In frequency domain, y(k) for kth subcarrier corresponds to y(k) = x(k)h(k) + w(k),

(6.10)

where h(k)∼ CN (0, 1) and w(k)∼ CN (0, N0 ) represent frequency response of multipath channel and AWGN, respectively. Later, OFDM block is divided into OFDM-IM subblocks as shown in Figure 6.3. In order to recover data bits conveyed by both the modulated subcarriers and their

(0)

yt

1 (1)

(0)

. . 1(

. .

. .

( )

( )

. .

. .

)

Block separation

OFDM-IM subblock

Maximum likelihood or LLR receiver

. . .

. . .

OFDM-IM subblock

Maximum likelihood or LLR receiver

p bits

(1) . .

(

− 1)

(

− 1)

( )

Figure 6.3 Block diagram of OFDM-IM receiver

p bits

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Flexible and cognitive radio access technologies for 5G and beyond

indices, maximum likelihood and log-likelihood ratio (LLR) receiver are proposed for OFDM-IM transmission [3].

6.2.1 Maximum likelihood detector Maximum likelihood decoder completes a full search over all possible subblock realizations to perform a joint decision of the data bits transmitted via the subcarriers’ indices and M -ary symbols. Maximum likelihood receiver for gth subblock is given as (ˆig , sˆg ) = arg min

a−1

|yg (k) − hg (k)sg (ξ )|2 .

(6.11)

ξ =0

Due to the exhaustive search, maximum likelihood receiver suffers from high complexity with the order of O(2log2 (C(b,a)) M a ). To exemplify, a total of 2log2 (C(b,a)) M a = 27 25 = 4,096 possible subblock realizations are searched in the case of b = 10, a = 5, and M = 2.

6.2.2 Log-likelihood ratio detector In order to reduce the complexity of maximum likelihood technique, an LLR detector is proposed to estimate the indices of active subcarriers. The logarithmic ratio of a posteriori probabilities is calculated as M −1 i=0 P x(k) = si | y(k) , (6.12) λ(k) = ln P x(k) = 0 | y(k) −1 where P(x(k) = 0) = ((b − a)/b) and M i=0 P(x(k) = si ) = a/b denote a posteriori probability of a given subcarrier being used or null, respectively. Moreover, after utilizing Bayes’ rule, λ(k) values can be represented as M −1

|y(k)2 | |y(k) − h(k)si |2 λ(k) = ln(a) − ln(b − a) + . + ln exp − N0 N0 i=0 (6.13) In each subblock, a subcarriers with maximum λ values denote the indices of utilized subcarriers. Later, these indices are mapped to p1 bits, while p2 information bits are obtained via the conventional demodulation of M -ary symbols.

6.3 State-of-the-art OFDM-IM solutions Promising advantages of OFDM-IM, such as high SE, high-energy efficiency, and improved reliability, to meet the diverse requirements of 5G and beyond communication networks shed light on the investigation of novel frequency-domain IM techniques [5]. In this section, state-of-the-art advances for OFDM-IM, including generalized, dual/multiple modes, and interleaved schemes, are elaborated. Furthermore, their implementation difficulties in practical scenarios are discussed.

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181

6.3.1 Interleaved OFDM-IM OFDM-ISIM is proposed to ensure that subcarriers in an OFDM-IM subblock pass through the uncorrelated fading channels. For this reason, OFDM-ISIM performs subcarrier-level block interleaving [6]. The performance of OFDM-IM systems depends on the correct detection of active subcarriers. The use of interleaving in OFDM-IM maximizes the Euclidean distance between two subblock realizations and thus provides better performance than the conventional OFDM-IM, which has no interleaving process. Since the implementation of frequency-domain interleaving corresponds to only a matrix multiplication, it is applied in the recently introduced OFDM-IM techniques.

6.3.2 Generalized OFDM-IM OFDM-IM outperforms OFDM in terms of SE in case low-order modulation is used. Otherwise, OFDM-IM may not provide higher SE than OFDM due to inactive subcarriers. Therefore, OFDM-GIM aims to increase the SE of conventional OFDM-IM without compromising its advantages [7]. In conventional OFDM-IM, the number of active subcarriers in a subblock is fixed to a. OFDM-GIM provides a degree of freedom to achieve flexible OFDMIM-based communication systems with high SE by activating a different number of subcarriers in each subblock. Mapping of information bits to subcarriers in OFDMIM and OFDM-GIM is illustrated in Figures 6.4 and 6.5, respectively. In OFDM-GIM, v is the set of allowed numbers of active subcarriers in each OFDM-IM subblock and expressed as v = [v(1) v(2) · · · v(σ )]1×σ ,

(6.14)

where v(σ ) ∈ {1, 2, . . . , b}. In the case of v(σ ) = b, all the subcarriers are activated in the subblock, as in the conventional OFDM transmission. For example, v = [2 3]1×2

1

2

3

4

...

1

2

X1

3

4

...

1

2

3

4

Freq.

XG

Xg

Figure 6.4 OFDM-IM with fixed subcarrier activation ratio

1

2

3 X1

4

...

1

2

3 Xg

4

...

1

2

3

4

Freq.

XG

Figure 6.5 OFDM-GIM with variable subcarrier activation ratio

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Flexible and cognitive radio access technologies for 5G and beyond

means that σ = 2, and two or three subcarriers can be activated for a given subblock. Therefore, the number of bits transmitted over the lth subblock is p = log2 (C(b, v(σ ))) + v(σ )log2 (M ).

(6.15)

In order to activate the different number of active subcarriers in a subblock, the lengths of p1 and p2 bit streams are changed for the selected v(σ ), while p is fixed for all subblocks to avoid error propagation stemming from subblock asynchronization. In the conventional OFDM-IM, maximum likelihood and LLR receivers can be used for the detection of data bits carried by both indices and M -ary symbols. As in (6.11), the maximum likelihood detector performs a search over all possible subblock realizations. Since the number of subblock realizations in OFDM-GIM is higher than that of the conventional OFDM-IM, the maximum likelihood detector leads to heavy complexity. In order to use the LLR receiver for OFDM-GIM, λ(k) values in (6.12) are calculated for the set of v(σ ) as |y(k)2 | N0

−1 M 2 n 1 + ln exp − y(k) − h(k)si , N0 v(σ ) i=0

λ(k) = ln(v(σ )) − ln(b − v(σ )) +

(6.16)

where v(σ ) that minimizes the distance between estimated transmit signals and received signal denotes the correct number of active subcarriers for each subblock.

6.3.3 Dual-mode OFDM In OFDM-IM, subcarriers within a subblock are split into two parts as active and inactive ones according to the incoming data bits. Likewise, DM-OFDM also divides subcarriers into two parts, but both of them are activated by the use of different modulation sets S1 and S2 for a and b − a subcarriers, respectively [8]. An example mapping of information bits to subcarriers for OFDM-IM and DM-OFDM is given in Table 6.1. Table 6.1 An example for mapping the information bits to subcarriers by OFDM-IM and DM-OFDM while (b = 4, a = 2) OFDM-IM

DM-OFDM

p1

ig

xg

p1

ig

xg

[00]

1,2

[si sj 0 0]

[00]

1,2

[S1 (i) S1 (j) S2 (i) S2 (j)]

[01]

2,3

[0 si sj 0]

[01]

2,3

[S2 (i) S1 (i) S1 (j) S2 (j)]

[10]

3,4

[0 0 si sj ]

[10]

3,4

[S2 (i) S2 (j) S1 (i) S1 (j)]

[11]

1,4

[si 0 0 sj ]

[11]

1,4

[S1 (i) S2 (i) S2 (j) S1 (j)]

Index modulation-based flexible waveform design

183

Q

Q y2 y1

y

–x

x

I

I –x2

x1

–x1

–y

x2

–y1 –y2

(a)

(b)

Figure 6.6 Constellation diagram for (a) OFDM-IM and (b) OFDM-GIM

The number of transmitted bits by DM-OFDM subblock is p = log2 (C(b, a)) + alog2 (M1 ) + (b − a)log2 (M2 ),

(6.17)

where M1 and M2 denote the size of S1 and S2 , respectively. Figure 6.6 shows the constellation diagrams for OFDM-IM and DM-OFDM. OFDM-IM has a constellation point at 0 due to inactive subcarriers. If Euclidean distance between the possible subblock realizations is higher than that of OFDM-IM, DM-OFDM provides better bit error rate (BER) performance, and vice versa. At the receiver, the maximum likelihood or LLR detector can be used for the recovering of the transmitted data. The minimum distance between the different subblock realizations is calculated to decide the used modulation set for a subcarrier. As an advanced version of DM-OFDM, multiple-mode OFDM (MM-OFDM) is proposed in the literature [9]. MM-OFDM uses multiple QAM/PSK sets within a subblock to enhance the SE.

6.3.4 Coordinate interleaved OFDM-IM Coordinate interleaved (CI) OFDM-IM (CI-OFDM-IM) combines OFDM-IM and space–time block codes with coordinate interleaving [10]. CI-OFDM-IM achieves an additional diversity gain through the transmission of real and imaginary parts of a complex data symbol over two active subcarriers via the CI orthogonal design. A rotated square M -ary QAM constellation is used in CI-OFDM-IM for the modulation. In Table 6.2, an example mapping of information bits to subcarriers is shown for CI-OFDM-IM. In addition to coordinate interleaving, block interleaving is also used in CI-OFDM-IM. In this way, CI-OFDM-IM provides better performance than both OFDM-IM and OFDM-ISIM. At the receiver, the maximum likelihood or LLR detector can be utilized for the detection of both active subcarriers and M -ary symbols.

184

Flexible and cognitive radio access technologies for 5G and beyond

Table 6.2 An example for mapping the information bits to subcarriers by CI-OFDM-IM while (b = 4, a = 2) CI-OFDM-IM p1

ig

xg

[00]

1,2

[siR + jsjI

[01]

2,3

[0

siR

+

sjR + jsiI jsjI

siR

[10]

3,4

[0

0

[11]

1,4

[siR + jsjI

+ 0

sjR jsjI

0 0]

+ jsiI sjR

0]

+ jsiI ]

0 sjR + jsiI ]

Flexible OFDM with IM

Subcarrier mapping scheme

Subcarrier activation ratio

Avoiding deep fading

Equal bit protection Robustness against asynchronous transmission

Securing communication link

Avoiding doppler spread

Robustness against hardware imperfection

Figure 6.7 Flexible features of OFDM-IM

6.4 Flexible OFDM with IM Different from OFDM, the uniqueness of OFDM-IM comes from its inherent flexible structure. In order to accommodate a wide range of services and applications for next-generations, adaptive solutions are needed. As shown in Figure 6.7, subcarrier mapping scheme and subcarrier activation ratio are the two main features of OFDMIM that can be controlled to support the user demands. In this section, the exploitation of these features is discussed in order to investigate the potential of OFDM-IM for beyond 5G networks.

Index modulation-based flexible waveform design

185

6.4.1 Subcarrier mapping scheme In the literature, it is shown that the way of how information bits are mapped to subcarriers impacts the performance of OFDM-IM, its robustness against interference, channel impairments, and hardware imperfections. Motivated by this, recently proposed subcarrier mapping schemes are discussed in this subsection. Initially, the design of OFDM-IM scheme is based on the idea of mapping index bits p1 to subcarriers with the aid of a look-up table [3]. The presence of the same table is assumed at the transmitter and receiver. The look-up table should contain all possible combinations of the active subcarrier indices ig and M -ary symbols. Tables 6.1 and 6.2 represent examples of look-up table. At the receiver, a joint decision of data bits transmitted by indices of active subcarriers (p1 ) and M -ary modulated symbols (p2 ) is performed by an maximum likelihood detector, as in (6.11). Thus, the complexity of look-up table significantly increases with the length of p1 bit stream and modulation order that limit the utilization of large-sized OFDM-IM subblocks. In order to use large subblock size for data transmission, a new mapping scheme named combinatorial (COMB) technique is proposed in the literature [3]. This scheme does not require storage tables at the transmitter and receiver. A specific lexicographically ordered sequence ηz = {vb · · · v1 } is assigned to each possible subblock realization ig , given that z ∈ {1, 2, . . . , 2p1 } and v ∈ {0, . . . , b}. First, p1 -bit stream is converted to a natural number (Z), which is mapped to a given ηz sequence as follows: Z = C(va , a) + · · · + C (v1 , 1),

b > va > · · · v1 ≥ 0,

(6.18)

where the selection of ηz components starts with the condition that satisfies Z ≥ C(va , a) and then chooses the maximal vb−1 that satisfies Z − C(va , a) ≥ C(va−1 , a − 1) until a = 1. Finally, each index realization is assigned to a unique p1 information bit stream. An example of OFDM-IM with (b = 8, a = 4) and p1 = [100000], elements of ηz = {6, 5, 4, 1} can be calculated as follows: 32 = C(6, 4) + C(5, 3) + C(4, 2) + C(1, 1).

(6.19)

Mapping of information bits to subcarriers by COMB scheme is given in Table 6.3 for (b = 8, a = 3). At the receiver, in contrast to the look-up table scheme where the maximum likelihood detector is required, the LLR detector in (6.13) can be used to determine the transmitted bits through active indices that are easily converted to ˆ considering (6.18). Lastly, a simple decimal-to-binary converter decimal number (Z) is applied over Zˆ to get p1 bit stream.

6.4.1.1 Equal bit protection Even though the COMB scheme avoids the use of a look-up table for mapping information to subcarriers, it leads to a nonuniform probability of subcarrier activation that prevents the OFDM-IM system from reaching the ultimate BER performance. It gives higher activation probability to initial subcarriers in a subblock. For instance, in the case of (b = 8, a = 3), Pact (1) and Pact (8) subcarriers are 15/32 and 0, respectively, as given in Table 6.4.

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Flexible and cognitive radio access technologies for 5G and beyond

Table 6.3 Mapping of information bits to subcarriers by COMB while (b = 8, a = 3) COMB p1

ig

p1

ig

[00000] [00001] [00010] [00011] [00100] [00101] [00110] [00111] [01000] [01001] [01010] [01011] [01100] [01101] [01110] [01111]

1, 2, 3 1, 2, 4 1, 3, 4 2, 3, 4 1, 2, 5 1, 3, 5 2, 3, 5 1, 4, 5 2, 4, 5 3, 4, 5 1, 2, 6 1, 3, 6 2, 3, 6 1, 4, 6 2, 4, 6 3, 4, 6

[10000] [10001] [10010] [10011] [10100] [10101] [10110] [10111] [11000] [11001] [11010] [11011] [11100] [11101] [11110] [11111]

1, 5, 6 2, 5, 6 3, 5, 6 4, 5, 6 1, 2, 7 1, 3, 7 2, 3, 7 1, 4, 7 2, 4, 7 3, 4, 7 1, 5, 7 2, 5, 7 3, 5, 7 4, 5, 7 1, 6, 7 2, 6, 7

Table 6.4 Subcarrier activation probability by COMB while (b = 8, a = 3) k

1

2

3

4

5

6

7

8

Pact (k)

15/32

15/32

7/16

7/16

7/16

3/8

3/8

0

Motivated by this, equal subcarrier activation (ESA) is proposed to enable an equal probable subcarrier activation [11]. A look-up table named as adjacent subcarrier distance vector (ASDV) is required at the transmitter and receiver to find C(b − 1, a − 1) basic combinations (iβ ). Column cyclic shift is used to obtain b − 1 new active subcarrier combinations from the iβ . It is important to note that new combinations have the same ASDV with the corresponding basic pattern. Thus, subcarrier combinations generated from cyclic shifts of iβ can be the same. In this case, only one of the repeated patterns is considered. This process is successively performed for all basic combinations to obtain 2p1 possible subcarrier combinations. At the receiver, the LLR detector is used to find ig and M -ary symbols that are transformed to p1 and p2 bit streams, respectively. In the case of (b = 8, a = 3), mapping of information bits to subcarriers by the ESA scheme is given in Table 6.5, and activation probability of all subcarriers within a subblock equals Pact (k) = 3/8. In this way, OFDM-IM with an ESA mapper achieves the ultimate BER performance due to equal bit protection.

Index modulation-based flexible waveform design

187

Table 6.5 Mapping of information bits to subcarriers by ESA while (b = 8, a = 3) ESA p1

ig

p1

ig

[00000] [00001] [00010] [00011] [00100] [00101] [00110] [00111] [01000] [01001] [01010] [01011] [01100] [01101] [01110] [01111]

1, 2, 3 2, 3, 4 3, 4, 5 4, 5, 6 5, 6, 7 6, 7, 8 7, 8, 1 8, 1, 2 1, 2, 4 2, 3, 5 3, 4, 6 4, 5, 7 5, 6, 8 6, 7, 1 7, 8, 2 8, 1, 3

[10000] [10001] [10010] [10011] [10100] [10101] [10110] [10111] [11000] [11001] [11010] [11011] [11100] [11101] [11110] [11111]

1, 2, 5 2, 3, 6 3, 4, 7 4, 5, 8 5, 6, 1 6, 7, 2 7, 8, 3 8, 1, 4 1, 2, 6 2, 3, 7 3, 4, 8 4, 5, 1 5, 6, 2 6, 7, 3 7, 8, 4 8, 1, 5

6.4.1.2 Robustness against asynchronous transmission COMB and ESA subcarrier mapping schemes are designed for the synchronous transmission of multiple users, where there is no interference coming from adjacent users. Unfortunately, OFDM-based systems struggle to meet the demands of next-generation communication systems, where a massive number of users systems, users must be aligned in both time and frequency dimensions to maintain the orthogonality between the OFDM subcarriers. However, synchronous communication cannot be guaranteed for uplink transmission since signals transmitted from the users at different distances from the base station arrive with different time delays. Moreover, users with mMTC in 5G and beyond networks have intermittent traffic structure. The time offset between users results in interference among subcarriers of adjacent users. Several techniques are proposed in the literature to mitigate inter-user interference (IUI). In [12,13], guard-band between the users is used to suppress out-of-band emission (OOBE), which defines the amount of energy leakage from adjacent users to the desired one. Moreover, different robust waveforms against IUI, such as filter bank multicarrier, generalized frequency division multiplexing, and universal filtered multicarrier are heavily investigated in the literature, but filtering process significantly increases the system complexity [14–17]. In [18], it is shown that the majority of OOBE stems from the edge subcarriers of the OFDM block. Motivated by the flexible structure of OFDM-IM, where active subcarriers can be adaptively decided via mapping scheme, in [19], a new mapping scheme named inner-subcarrier activation (ISA) is proposed for asynchronous communication networks. ISA scheme offers activation priority for the subcarriers located

188

Flexible and cognitive radio access technologies for 5G and beyond

at the center part of the OFDM-IM subblock. The obtained subcarrier activation probabilities through COMB, ESA, and ISA schemes are shown in Figure 6.8. The ISA mapper is based on a COMB mapper. A subblock with b subcarriers is split into two parts, where first and second parts contain b1 and b2 subcarriers, and a1 and a2 subcarriers are activated, respectively. Indices for a1 subcarriers i1 are selected by the inverse COMB method, while the conventional COMB method is used to select a2 subcarrier indices i2 . Consequently, indices of active subcarriers for gth subblock are composed of i1 and i2 . In this way, p1 equals p1 = log2 (C(s1 , v1 )) + log2 (C(s2 , v2 )) ≤ log2 (C(s, v)).

(6.20)

At the receiver, the LLR detector is used to find the active subcarrier indices. The subcarrier index patterns are converted to lexicographically ordered sequences ηz,1 and ηz,2 . By using (6.18), these sequences are mapped to a decimal numbers E1 and E2 and then the transmitted bits are obtained. It is shown that the desired user faces less IUI with the aid of the ISA mapper since OOBE caused by inner subcarriers is less than that of edge subcarriers. Due the fact that the ISA scheme is based on COMB scheme, ISA mapper does not bring additional complexity to the system. It offers the same computational complexity with COMB mapper. Unlike ESA scheme, look-up table is not needed for ISA mapper.

6.4.1.3 Avoiding deep fading Deep fading effect of the wireless channel leads to severe performance degradation in OFDM-based systems. Therefore, forward error correction (FEC) codes are used in communication systems to avoid errors due to the channel. Convolutional codes, low-density parity-check codes, and turbo codes are the most known FEC techniques. Moreover, interleaving is applied to avoid burst errors caused by deep fading on successive subcarriers. In the case of consecutive error, FEC codes cannot correct the errors properly. 0.25 COMB ESA ISA

0.2

Subcarrier activation probability

Subcarrier activation probability

0.25

0.15

0.1

0.05

0 1

2

3

4 5 6 Subcarrier index

(a)

7

8

COMB ESA ISA

0.2

0.15

0.1

0.05

0 1

2

3

4 5 6 Subcarrier index

7

8

(b)

Figure 6.8 Subcarrier usage probability within an OFDM-IM subblock for the three SMSs regarding to different (b,a): (a) (b = 8, a = 3) and (b) (b = 8, a = 4)

Index modulation-based flexible waveform design

189

The presence of unutilized subcarriers in OFDM-IM enables the mitigation of the deep fading effect in OFDM-based transmission. In [20], the alignment of high-gain subchannels with the active subcarriers is performed by means of the circular shift. In this way, the effect of deeply faded subchannels is controlled. Although the look-up table is initially used for the mapping of information bits to subcarriers, it is modified by shift operation considering the channel fades. It is shown that OFDM-IM with the channel-aligned mapper can further enhance system performance in terms of BER. However, channel-based mapping scheme requires the knowledge of channel state information at the transmitter.

6.4.2 Subcarrier activation ratio Different from the conventional OFDM, in OFDM-IM, SE is dependent on both the modulation order and the number of carried bits by indices. Although the number of conveyed bits by subcarrier increases with the high subcarrier activation ratio, the number of carried bits by their indices is limited and decreases after a certain number of active subcarriers, as shown in Figure 6.9. Therefore, the fractional subcarrier activation ratio should be exploited considering the other issues faced in communication systems, such as channel impairments, hardware imperfection, and securing the communication link.

6.4.2.1 Avoiding Doppler spread High-speed vehicle-to-everything (V2X) communication is one of the most challenging services in 5G and beyond networks. Unlike fix-to-mobile transmission in 4G, mobile-to-mobile transmission in 5G and beyond puts pressure on service providers to satisfy the user demands along with the mobility. The current OFDM technology is sensitive to time-selective channels due to Doppler spread that destroys the 18

8 BPSK + indices QPSK + indices Only indices

14

6 5 4 OFDM

3 2 1

OFDM

12 10 8 6

OFDM

4 2

0

0 1

(a)

BPSK + indices QPSK + indices Only indices

16

OFDM Spectral efficiency (bps/Hz)

Spectral efficiency (bps/Hz)

7

2 3 Number of active subcarriers

4

1

2

3

4

5

6

7

Number of active subcarriers (b)

Figure 6.9 Number of transmitted bits per OFDM-IM subblock considering different modulation orders and number of active subcarriers: (a) (b = 4) and (b) (b = 8)

8

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Flexible and cognitive radio access technologies for 5G and beyond

orthogonality between the subcarriers in the frequency domain and leads to intercarrier interference (ICI). In [3], it is shown that OFDM-IM is more robust than OFDM in the case of high mobility due to partial subcarrier activation. Hence, in [21], OFDM-IM is proposed as an alternative solution for V2X and railway communications. Moreover, ICI is analyzed and mitigated using optimal tone spacing between adjacent subcarriers in [22]. In this way, OFDM-IM allows controlling the number of active subcarriers in order to mitigate the effect of ICI.

6.4.2.2 Robustness against hardware imperfection OFDM is sensitive to hardware impairments, including carrier frequency offset (CFO) and in-phase and quadrature imbalance (IQI) [23,24]. CFO is the mismatch between local oscillators of transmitter and receiver that leads to ICI between the OFDM subcarriers. OFDM-based systems cannot maintain the orthogonality in the presence of ICI. Since not all the subcarriers are utilized in OFDM-IM, the impact of ICI is reduced compared to OFDM [25]. On the other hand, IQI corresponds to mismatches on the amplitude and phase response on I and Q branches of local oscillators [26]. In the presence of IQI, kth subcarrier will cause interference on its mirror subcarrier and vice versa. In [27], the effect of IQI is investigated for OFDM-IM, and a simple non-iterative receiver is proposed to mitigate this impairment. Since inactive subcarriers only contain the interference stemming from IQI, it can be easily estimated and mitigated. However, the estimation and compensation of IQI require an iterative complex receiver design in OFDM. In [28], the performance of OFDM-IM and OFDM-GIM is compared in case CFO and an IQI exist in the system. It is shown that due to the higher subcarrier activation ratio, OFDM-GIM is more affected than OFDM-IM.

6.4.2.3 Securing communication link Physical layer security (PLS) is considered as an attractive approach to secure the communication link, instead of securing the communication as in the encryptionbased solutions. The idea behind PLS relies on the exploitation of the wireless channel uniqueness both to guarantee successful transmission for the desired user and to avoid eavesdropping. In the literature, in order to achieve secrecy for the intended user, MIMO signaling, relay-aided systems, and coordinated multipoint transmission are studied from the perspective of PLS. Different from OFDM, the transmission of information bits through partial subcarrier activation in OFDM-IM gives a chance to exploit the channel and consequently achieve the secrecy gap between the intended user and eavesdropper. In [29], inspired by OFDM-IM, OFDM with subcarrier index selection (OFDM-SIS) is introduced. In OFDM-SIS, subchannels within an OFDM block pass through the interleaver in order to break the correlation between them. Then, subcarrier index selection is performed considering their gains. Since the activation of subcarriers is dependent on the channel that is different for the intended user and eavesdropper, it is shown that a significant secrecy gap is achieved along with the increase in BER performance of the desired user. It is important to note that OFDM-SIS does not transmit information bits through subcarrier indices. Moreover, in [30], adaptive OFDM-IM is introduced in order to achieve secure communication. The look-up table is used as a subcarrier mapping

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scheme. Two different adaptation schemes are proposed: OFDM with adaptive IM and fixed constellation modulation (OFDM-AIM-FCM), and OFDM with adaptive IM and adaptive constellation modulation (OFDM-AIM-ACM). In OFDM-AIM-FCM and OFDM-AIM-ACM, the subcarrier activation ratio is decided considering the subchannel gains, and it changes for all OFDM-IM subblocks. The same modulation is used for the activated subcarriers in OFDM-AIM-FCM, while OFDM-AIM-ACM splits the subchannel gains within the subblock into four levels, which each of them corresponds to different modulation orders.

6.5 Discussions and future directions The potential of reviewed frequency-domain IM types for beyond 5G networks is discussed in this section. Then, their potential for three main services of 5G networks, including eMBB, mMTC, and URLLC, are elaborated regarding their advantages and disadvantages. Frequency-domain IM types do not require extra hardware for their practical implementation. Their pros and cons are presented in Table 6.6. The complexity of conventional OFDM-IM with LLR detector is the same with OFDM in terms of complex multiplications [3]. In OFDM-ISIM and CI-OFDM-IM, block-interleaving corresponds to the multiplication of OFDM block with an interleaving matrix. It is easily applied in the frequency domain in order to break the correlation between the active subcarriers. Due to the advanced receiver design and high subcarrier activation ratio, OFDM-GIM is sensitive to the channel and hardware impairments and loses the fundamental advantages of IM, including energy efficiency and reliability. Table 6.6 The comparison of state-of-the-art frequency-domain IM techniques IM types

Advantages

Disadvantages

OFDM-IM [3]

Robustness against ICI, hardware impairments, including CFO and IQI Exploiting frequency diversity with the aid of interleaving, reliable transmission Allowing a different number of active subcarriers for different subblocks, higher SE than OFDM-IM

Lower SE than conventional OFDM for high-order modulations Lower SE than conventional OFDM for high-order modulations Two-stage Rx process, error propagation, sensitivity to hardware impairments

DM-OFDM [8]

Use of two different modulation schemes within a subblock, higher SE than OFDM-IM

Sensitivity to hardware impairments

CI-OFDM-IM [10]

Exploiting the real frequency diversity, reliable transmission

Lower SE than the conventional OFDM for high-order modulations

OFDM-ISIM [6]

OFDM-GIM [7]

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The primary requirement for eMBB is the maximization of SE. OFDM-IM is superior to the conventional OFDM in terms of the BER performance, but it offers less SE with the increase of modulation order. DM-OFDM provides a higher data rate than the classical OFDM by filling the passive subcarriers of OFDM-IM with different modulations, and thus it is highly suitable for eMBB use cases. Moreover, MM-OFDM enables the activation of all subcarriers by using multiple QAM/PSK sets, which can be used for high data rate eMBB applications. mMTC applications and use cases require a large coverage area, low-power consumption, and low-cost device. SE and reliability are not the primary requirements for the mMTC. Partial subcarrier activation in OFDM-IM inherently provides higher energy-efficiency than OFDM. For example, OFDM-IM with (b = 8, a = 3) conveys p1 = 5 bits by the indices of active subcarriers and p2 = 3 bits by the modulated subcarriers with binary PSK (BPSK). However, the classical OFDM requires eight active subcarriers to transmit p1 + p2 = 8 bits. Hence, OFDM-IM with (b = 8, a = 3) can harvest 66% of the Tx power to transmit the same number of data bits. On the other hand, since DM-OFDM and MM-OFDM activate all subcarriers, they cannot provide energy efficiency compared with OFDM. In order to satisfy URLLC, diversity-based techniques are required. Therefore, ISIM-OFDM and CI-OFDM can be considered as potential solutions. They offer higher reliability than conventional OFDM due to block-interleaving. Moreover, CI-OFDM-IM achieves an additional diversity gain through coordinate interleaving. Lastly, partial subcarrier activation in OFDM-IM allows designing interference immune transmission for URLLC [31].

6.6 Conclusion This chapter is dedicated to the understanding of frequency-domain IM concept that is considered as a potential for beyond 5G wireless technologies. The current OFDM technology struggles to meet the diverse requirements of next-generation communication systems due to its inflexible structure. Advances in the frequency-domain IM are discussed to present the ways of achieving flexibility in OFDM-based systems. Therefore, OFDM-IM can be considered as a complementary waveform to the conventional OFDM. Moreover, it is assessed that how two main features of OFDM-IM including subcarrier mapping scheme and subcarrier activation ratio are utilized in the literature to overcome the different problems of wireless communications systems, such as channel impairments and hardware imperfection. Lastly, the utilization of reviewed IM schemes for next-generation services including eMBB, mMTC and URLLC is envisioned.

Acknowledgment This work was supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 218E035.

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References [1] Abu-alhiga R, and Haas H. Subcarrier-index modulation OFDM. In: Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC); 2009. p. 177–181. [2] Tsonev D, Sinanovic S, and Haas H. Enhanced subcarrier index modulation (SIM) OFDM. In: Proc. IEEE Global Telecommunication Conference Workshops (GLOBECOM); 2011. p. 728–732. [3] Basar E, Aygolu U, Panayirci E, et al. Orthogonal frequency division multiplexing with index modulation. IEEE Transactions on Signal Processing. 2013;61(22):5536–5549. [4] Basar E. Index modulation techniques for 5G wireless networks. IEEE Communications Magazine. 2016;54(7):168–175. [5] Basar E, Wen M, Mesleh R, et al. Index modulation techniques for nextgeneration wireless networks. IEEE Access. 2017;5(8):16693–16746. [6] Xiao Y, Wang S, Dan L, et al. OFDM with interleaved subcarrier-index modulation. IEEE Communications Letters. 2014;18(8):1447–1450. [7] Fan R, Yu YJ, and Guan YL. Orthogonal frequency division multiplexing with generalized index modulation. In: Proc. IEEE Global Communications Conference (GLOBECOM); 2014. p. 3880–3885. [8] Mao T, Wang Z, Wang Q, et al. Dual-mode index modulation aided OFDM. IEEE Access. 2017;5(8):50–60. [9] Wen M, Basar E, Li Q, et al. Multiple-mode orthogonal frequency division multiplexing with index modulation. IEEE Transactions on Communications. 2017;65(9):3892–3906. [10] Basar E. OFDM with index modulation using coordinate interleaving. IEEE Wireless Communications Letters. 2015;4(4):381–384. [11] Wen M, Zhang Y, Li J, et al. Equiprobable subcarrier activation method for OFDM with index modulation. IEEE Communications Letters. 2016; 20(12):2386–2389. [12] Weissa T, Hillenbrand J, Krohn A, et al. Mutual interference in OFDM-based spectrum pooling systems. In: Proc. IEEE Vehicular Technology Conference (VTC); 2004. p. 1873–1877. [13] Demir AF, and Arslan H. The impact of adaptive guards for 5G and beyond. In: Proc. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2017. p. 1–5. [14] Farhang-Boroujeny B. OFDM versus filter bank multicarrier. IEEE Signal Processing Magazine. 2011;28(3):92–112. [15] Wunder G, Jung P, Kasparick M, et al. 5G NOW: Non-orthogonal, asynchronous waveforms for future mobile applications. IEEE Communications Magazine. 2014;52(2):97–105. [16] Wang S, Armstrong J, and Thompson JS. Waveform performance for asynchronous wireless 5G uplink communications. In: Proc. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC); 2016. p. 1–6.

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Eeckhaute MV, Bourdoux A, Doncker PD, et al. Performance of emerging multi-carrier waveforms for 5G asynchronous communications. EURASIP Journal on Wireless Communications and Networking. 2017;2017(1). Sahin A, and Arslan H. Edge windowing for OFDM based systems. IEEE Communications Letters. 2011;15(11):1208–1211. Dogan S, Tusha A, Arslan H. OFDM with index modulation for asynchronous mMTC networks. Sensors. 2018;18(4):1280. Memisoglu E, Basar E, and Arslan H. Fading-aligned OFDM with index modulation for mMTC services. Physical Communication. 2019;35:100680. Cheng X, Wen M, Yang L, et al. Index modulated OFDM with interleaved grouping for V2X communications. In: Proc. International IEEE Conference on Intelligent Transportation Systems (ITSC); 2014. p. 1097–1104. Ntouni GD, Kapinas VM, and Karagiannidis GK. On the optimal tone spacing for interference mitigation in OFDM-IM systems. IEEE Communications Letters. 2017;21(5):1019–1022. Arslan H. IQ gain imbalance measurement for OFDM based wireless communication systems. In: Proc. IEEE Military Communications Conference (MILCOM); 2006. p. 1–5. Mahmoud HA, Arslan H, Ozdemir MK, et al. IQ imbalance correction for OFDMA uplink systems. In: Proc. IEEE International Conference on Communications (ICC); 2009. p. 1–5. Ma Q,Yang P, XiaoY, et al. Error probability analysis of OFDM-IM with carrier frequency offset. IEEE Communications Letters. 2016;20(12):2434–2437. Tubbax J, Come B, der Perre LV, et al. Compensation of IQ imbalance in OFDM systems. In: Proc. IEEE International Conference on Communications (ICC); 2003. p. 1–9. Tusha A, Dogan S, and Arslan H. IQI mitigation for narrowband IoT systems with OFDM-IM. IEEE Access. 2018;6(8):44626–44634. Tusha A, Dogan S, and Arslan H. Performance analysis of frequency domain IM schemes under CFO and IQ imbalance. In: Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC); 2019. p. 1–5. Hamamreh JM, Basar E, and Arslan H. OFDM-subcarrier index selection for enhancing security and reliability of 5G URLLC services. IEEE Access. 2017;5(11):25863–25875. Furqan HM, Hamamreh JM, and Arslan H. Adaptive OFDM-IM for enhancing physical layer security and spectral efficiency of future wireless networks. Wireless Communications and Mobile Computing. 2018;2018:1–16. Dogan S, Tusha A, and Arslan H. NOMA with index modulation for uplink URLLC through grant-free access. IEEE Journal of Selected Topics in Signal Processing. 2019;13(6):1249–1257.

[18] [19] [20] [21]

[22]

[23]

[24]

[25] [26]

[27] [28]

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[30]

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Part III

Multiple antenna systems for 5G and beyond

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Chapter 7

Massive MIMO for 5G and beyond Jingbo Tan1 , Xiuhong Wei1 , Shuangkaisheng Bi1 , Mingyao Cui1 , and Linglong Dai1

Massive multiple-input multiple-output (MIMO) is one of the key technologies for fifth generation (5G) and beyond [1]. By utilizing a large number of antennas, it can significantly improve the spectrum efficiency and is promising to achieve 5G key performance indicators. However, the application of large-scale antenna arrays leads to new challenges for physical layer signal processing. In this chapter, we will focus on massive MIMO systems and give a comprehensive introduction on massive MIMO technology for 5G and beyond.

7.1 Introduction of massive MIMO With the rapid development of global mobile network and Internet of Things technology, the demand of data has grown explosively. The fourth generation (4G) mobile communication system is difficult to meet the thousand-fold growth of data demand. In order to meet such a rapid network capacity growth, the 5G mobile communication system has drawn lots of attention [2]. The key performance indicator of 5G is to achieve a thousand-fold increase in communication capacity with low-energy consumption. Massive MIMO is widely considered as one of the key technologies to achieve this goal [1]. Massive MIMO, by exploiting hundreds or thousands of antennas at the base station (BS), introduces large spatial degrees of freedom, which can improve spectral and energy efficiency by several orders of magnitude. On the other hand, due to the deployment of large-scale antenna arrays, massive MIMO channel enjoys characteristics of asymptotic orthogonality, which greatly simplifies the signal-processing complexity of MIMO transceivers. Since it was proposed in 2010 [3], massive MIMO technology has become a research hotspot in academia and industry and has been formally adopted as one of the 5G physical layer technologies in the 3rd Generation Partnership Project (3GPP) New Radio (NR) R13, 14 standards. Although huge performance improvement can be achieved by massive MIMO, the application of a large number of antennas introduces new challenges for physical layer

1

Department of Electronic Engineering, Tsinghua University, Beijing, P.R. China

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design. At first, the traditional signal detection methods [4], which are based on matrix inversion, will cost huge complexity with a large number of antennas. Second, channel state information (CSI) that is essential for channel-adaptive techniques is of large dimension. Therefore, the classical linear channel acquisition schemes, including channel estimation and channel feedback, require unacceptable training overhead [5]. Third, to fully exploit the spatial multiplexing gain of massive MIMO, precoding becomes an important technique. However, the conventional digital precoding, which requires an equal number of radio frequency (RF) chains and antennas, will induce huge power consumption [6]. The above challenges become obstacles for the practical application of massive MIMO. Till now, many researches have investigated the above topics and proposed feasible solutions. In this chapter, we attempt to provide a comprehensive introduction of massive MIMO. In Section 7.2, the theoretical basic of massive MIMO is introduced. In Section 7.3, we discuss the widely utilized massive MIMO channel models. Then, the key physical layer signal-processing techniques are introduced in Sections 7.4– 7.6, including signal detection, channel acquisition and precoding. After that, we introduce the present massive MIMO prototypes and testbeds in Section 7.7. Finally, we conclude the future challenges of massive MIMO in Section 7.8 and give the summary of this chapter in Section 7.9. Notation: Lower case and upper case boldface letters represent vectors and matrices, respectively; (·)T , (·)H , ·F and ·k denote the transpose, conjugate transpose, Frobenius norm and k-norm of a matrix, respectively; H(i, j) denotes the element of matrix H at the ith row and the jth column; E {·} denotes the expectation; |·| denotes the absolute operator; IN represents the identity matrix of size N × N ; diag(A) denotes block diagonal matrix where each column of A represents the diagonal elements; CN(μ, ) denotes the Gaussian distribution with mean μ and covariance .

7.2 Information theory of massive MIMO In this section, the fundamental theory of massive MIMO is first introduced. Then, the capacity and spectrum efficiency of massive MIMO is analyzed based on information theory, where both the situations with perfect CSI and imperfect CSI are considered.

7.2.1 Fundamental of massive MIMO The MIMO technology utilizes multiple antennas at both the BS side and the user equipment (UE) side. By employing multiple antennas, the spatial multiplexing gain can be fully obtained, which makes MIMO capable of increasing the channel capacity. Theoretical analysis has shown that the channel capacity increases as the number of antennas increases. This makes MIMO promising to meet the capacity growth demand of mobile communications. In Long Term Evolution (LTE) Advanced standard, the BS is first supported to exploit 2–8 antennas to improve the channel capacity [7]. However, the traditional MIMO technology faces bottleneck to further elevate the channel capacity, since it is believed that the signal-processing complexity will be unacceptable when the number of antennas becomes larger.

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Marzetta proposed the concept of massive MIMO in 2010 [3]. In massive MIMO, the BS equips thousands of antennas and supports tens of UEs. It has been proved that when the number of antennas tends to infinity, the channel of different users becomes orthogonal to each other. By utilizing this orthogonality, the signalprocessing complexity can be significantly reduced, which makes massive MIMO practical to increase the channel capacity by two orders of magnitude and becomes one of the key technologies of 5G network. Then, we will introduce the fundamental of massive MIMO. Consider a massive MIMO system with an M -antenna BS serving K singleantenna UEs simultaneously, as shown in Figure 7.1. Under the time division duplex (TDD) mode, the uplink received signal y ∈ CM ×1 can be denoted as y=

√ pGx + n,

(7.1)

where p is the transmission power of UEs, x ∈ CK×1 is the transmitted signals of K UEs satisfying E(xH x) ≤ 1 and n ∈ CM ×1 is the noise vector with n ∼ CN(0, σ 2 IM ), where σ 2 denotes the noise power. G ∈ CM ×K represents the downlink channel, the kth (k = 1, 2, . . . , K) column of which is the channel between the BS and the kth UE. Generally, the downlink channel G composes of large- and small-scale fading as G = HD1/2 ,

(7.2)

M antennas

UE 1

UE 2

UE 3

UE K

BS

Figure 7.1 The system model of massive MIMO systems

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where H ∈ CM ×K denotes the small-scale fading and the diagonal matrix D ∈ CK×K denotes the large-scale one, whose kth diagonal element βk represents the kth UE’s large-scale fading coefficient. According to (7.2), we have HH H 1/2 1 H (7.3) G G = D1/2 D . M M In sub-6G frequency, the small-scale fading of the propagation channel can be modeled as Rayleigh fading, which means each element of H follows i.i.d. complex Gaussian distribution with zero mean and unit variance. Therefore, for the pth UE’s small-scale fading hp = H( :, p) and the qth UE’s small-scale fading hq = H( :, q), when M → ∞, we can obtain 1 H 1 H a.s. a.s. h hp −→ 1, h hq −→ 0. (7.4) M p M q Thus, in massive MIMO systems with M → ∞, (7.3) has the following approximation as HH H 1/2 a.s. 1 H G G = D1/2 D −→ D. (7.5) M M Equation (7.5) indicates that in massive MIMO systems, the small-scale fading of the channel is eliminated due to a large number of antennas, and the channels of different users are approximately orthogonal, which means the inter-user interference is removed, as shown in Figure 7.2. This characteristic of massive MIMO as shown in (7.5) is called favorable propagation conditions. Under this condition (7.5), the linear signal-processing methods, such as zero-forcing (ZF) signal detection, can obtain near-optimal performance, which can significantly reduce the signal-processing complexity. 1

Interference

0.9

6×6 6 × 128

0.8 0.7 CDF

0.6 0.5 0.4 No interference

0.3 0.2 0.1 0 −40

−30

−20 −10 0 10 Ordered singular values (dB)

20

Figure 7.2 Left: The asymptotically orthogonality of massive MIMO channel; right: the CDF of the smallest and the largest singular value of the channel with antenna number 6 × 6 and 6 × 128

30

Massive MIMO for 5G and beyond

201

Then, we introduce the favorable propagation conditions in detail. Without losing generality, we ignore the large-scale fading as D = IK . Thus, the achievable rate R can be denoted as K H 2 log2 (1 + pλ ) , (7.6) R = E log2 IK + G G = E k

k=1

where λk , k = 1, 2, . . . , K, is the singular value of the small-scale fading matrix H. Consideringthat each element of H follows i.i.d. complex Gaussian distribution, we have E( Kk=1 λ2 ) = E(H2F ) = MK. Then, the achievable rate can be bounded as log2 (1 + MKp) ≤ R ≤ K log2 (1 + Mp).

(7.7)

When λ21 = MK, λ22 = · · · = λ2K = 0, the lower bound in (7.7) holds. When λ21 = · · · = λ2K = M , the upper bound of (7.7) holds, and the optimal achievable rate can be achieved. Under the favorable propagation conditions, H has K equal singular values due to HH H = M IK . Figure 7.2 illustrates the cumulative distribution function (CDF) of the singular values, where we can observe that as the number of antennas increases, the singular values of H become closer to each other. Therefore, we can conclude that in massive MIMO systems, the optimal achievable rate can be obtained, since by increasing the number of the antennas, the channel asymptotically satisfies the favorable propagation conditions with equal singular values.

7.2.2 Spectrum efficiency analysis of massive MIMO By employing large-scale antenna array, massive MIMO technology is able to significantly elevate the spectrum efficiency. In this section, we will theoretically analyze the spectrum efficiency of massive MIMO systems. Generally, in uplink transmission, the maximum likelihood detection can achieve optimal spectrum efficiency performance, whereas the complexity of maximum likelihood detection may increase exponentially with the number of UEs. Fortunately, since the UE’s channels are asymptotically orthogonal with each other, low-complexity linear signal detection methods such as maximum ratio combing (MRC), ZF and minimum mean square error (MMSE) can also obtain near-optimal spectrum efficiency performance [8]. Hence, we consider these linear signal detection methods in this section with perfect CSI and imperfect CSI.

7.2.2.1 Perfect CSI We consider the system model as shown in (7.1). The situation with perfect CSI is first introduced, where the BS is assumed to obtain the accurate channel G. Denote A ∈ CM ×K as the linear signal detection matrix. Using linear signal detection, we can recover the transmitted signal as r = AH y.

(7.8)

In ZF, MMSE and MRC detection, the matrix A can be denoted as G(GH G)−1 , G(GH G + p−1 I)−1 and G, respectively. Based on (7.1) and (7.8), we can obtain √ (7.9) r = pAH Gx + AH n.

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Denote rk = r(k) and xk = x(k) as the detected and transmitted signals of the kth UE. Then, we have K √ √ H ak gi xi + akH n, rk = pakH gk xk + p (7.10) i=1,i =k

where ak = A( :, k) and gk = G( :, k). Thus, the ergodic uplink achievable rate of the kth UE with perfect CSI RP,k can be described as

p akH gk H RP,k = E log2 1 + K . (7.11) a gi + ak p i=1,i =k

k

Lemma 7.1. Assume that the BS can obtain perfect CSI, and the UEs transmit signals with equal power p = E/M where E is fixed. Then, we have RP,k → log2 (1 + βk E), M → ∞.

(7.12)

Proof: Take MRC for instance where A = G, ak = gk . According to (7.11), the uplink achievable rate of the kth UE can be represented as

p gkH gk RP,k = E log2 1 + K . (7.13) p i=1,i =k gkH gi + gk Considering the channel characteristics of massive MIMO that when M → ∞, gkH gi = 0, gk 22 = M βk and p = E/M , (7.12) obviously holds. For the ZF and MMSE, we can reach the same conclusion since when M → ∞,GH G → M D. Lemma 7.1 shows that when the BS knows the perfect CSI and the UEs have equal transmission power, the massive MIMO systems is able to obtain the same achievable rate performance as a single-antenna interference-free system with transmission power E. This indicates that by utilizing large-scale antenna array at the BS, we can reduce the transmission power of each antenna to 1/M , which can reduce the performance requirements for hardware components such as power amplifier. Moreover, as the UEs’ achievable rate is only related to the large-scale fading βk in (7.13), K users can be served simultaneously in the same time–frequency resource with optimal achievable rate, which means massive MIMO is able to increase the spectrum efficiency K times. In practical massive MIMO systems, the number of antennas M is limited. Reference [8] has analyzed the achievable rate of massive MIMO systems with perfect CSI and limited M . Specifically, under perfect CSI and limited M , the achievable rate achieved by MRC, ZF and MMSE signal detection can be bounded, respectively, as p(M − 1)βk MRC RP,k ≥ log2 , (7.14) p Ki=1,i =k βi + 1 p(M − 1)βk ZF RP,k ≥ log2 , (7.15) p Ki=1,i =k βi + 1 RMMSE ≥ log2 (1 + (αk − 1)θk ) , P,k

(7.16)

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where αk = ((M − K + 1 + (K − 1)μ)2 /(M − K +1+(K −1)κ)) and θk = ((M − K + 1 + (K − 1)κ)/(M − K + 1 + (K − 1)μ))pβk in which αk and βk are determined by two equations as μ = (1/(K − 1)) Ki=1,i =k (Mpβi (1 + (K − 1/M )(μ − 1)) + 1)−1 and κ +(κpβi /( Ki=1,i =k (Mpβi (1+((K − 1)/M )(μ−1)) +1)2 ))= Ki=1,i =k (pβi μ+ 1 (Mpβi (1 + ((K − 1)/M )(μ − 1)) + 1)−2 /(K − 1). Equations (7.14)–(7.16) can be derived based on Jensen’s inequality and Rayleigh fading assumption of small-scale fading.

7.2.2.2 Imperfect CSI In this subsection, the capacity analysis of massive MIMO systems with imperfect CSI will be provided. In practical massive MIMO systems, the BS obtains the CSI through uplink channel estimation. Therefore, only imperfect CSI can be obtained by ˆ ∈ CM ×K as the CSI from the channel estimation. G ˆ can be denoted the BS. Denote G ˆ = G + E, where E is the channel estimation error. When MMSE method is as G utilized for uplink channel estimation, each element of the error matrix E can be modeled as i.i.d. complex Gaussian distribution with zero mean and variance of βi /(pτβi + 1), where τ is the number of pilots for uplink channel estimation. Then, the received signals at the BS rˆ can be denoted as ˆ H √pGx ˆ − √pEx + n , rˆ = A (7.17) ˆ is the linear signal detection matrix designed based on G. ˆ The kth UE’s where A received signal rˆk = rˆ (k) can be represented as rˆk =

K K √ H √ H √ H pˆak gˆ k xk + p aˆ k gˆ i xi − p aˆ k ei xi + aˆ kH n, i=1,i =k

(7.18)

i=1

ˆ :, k), eˆ k = E( :, k) and gˆ k = G( ˆ :, k). G ˆ and E are independent and A ˆ where aˆ k = A( and E are also independent. The BS treats the estimated channel as the true channel, and the last three terms in (7.18) are considered as interference. Therefore, the kth UE’s achievable rate of uplink transmission with imperfect CSI can be denoted as

p|ˆakH gˆ k |2 RIP,k = E log2 1 + K . p i=1,i =k |ˆakH gˆ i |2 + pˆak 22 Ki=1 (βi /τ pβi ) + ˆak 22 (7.19) Then, we first discuss the uplink achievable rate when M → ∞. The situation with limited M will be introduced later. The following lemma provides the power reduction by utilizing massive MIMO with imperfect CSI. Lemma 7.2. Assume that the BS obtains imperfect CSI through MMSE √ estimation from uplink pilots, and each UE transmits signals with equal power p = E/ M where E is fixed. Then, we have RP,k → log2 (1 + τβk2 E 2 ), M → ∞.

(7.20)

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Proof : For MRC signal detection, substituting aˆ k = gˆ k into (7.19), we can have the uplink achievable rate as

pˆgk 42 . RIP,k = E log2 1 + K p i=1,i =k |ˆgkH gˆ i |2 + pˆgk 22 Ki=1 (βi /τ pβi ) + ˆgk 22 (7.21) Notice that each element of gˆ k still satisfies i.i.d. complex Gaussian distribution with zero mean and variance of (τ pβk2 /(τ pβk + 1)). Thus, when M → ∞, the asympH 2 2 totically orthogonality still holds √ as gˆ k gˆ i = 0, ˆgk 2 = (M τ pβk /(τ pβk + 1)). Based on this property and p = (E/ M ), we can obtain (7.21). For the ZF and MMSE detection, the same conclusion can be approached by a similar procedure. Lemma 7.2 illustrates that with imperfect CSI and a large M , the performance of massive MIMO system with M antennas at the BS and equal transmission power √ (E/ M ) for each UE is equal to that of an interference-free single-input single-output system with transmission power τβk E 2 , without small-scale fading. Therefore, we can √ cut the transmission power proportionally to (1/ M ) without performance loss with imperfect CSI, which can still obtain K times spectrum efficiency increase. When M is limited, the achievable rate can be derived similarly to the situation with perfect CSI. Due to the limited space, we directly provide the final results of the achievable rate. The detailed analysis procedure can be found in [8]. Specifically, under imperfect CSI and limited M , the achievable rate obtained by MRC, ZF and MMSE detection can be represented as τ p(M − 1)βk2 MRC RIP,k ≥ log2 1 + , (7.22) (τ pβk + 1) Ki=1,i =k βi + (τ + 1)βk + (1/p) 2 2 τ p (M − K)β k RZF , (7.23) IP,k ≥ log2 1 + (τ pβk + 1) Ki=1 (pβi /(τ pβi + 1)) + τ pβk + 1 ˆ 1 + α ˆ , (7.24) ≥ log − 1 (θ) RMMSE k k 2 IP,k where αˆ k = (((M − K + 1 + (K − 1)μ) ˆ 2 )/(M − K + 1 + (K − 1)κ)) ˆ and ((M − K + ˆ 1 + (K − 1κ))/(M ˆ − K + 1 + (K − 1)μ))w ˆ βk , in which w = 1/( Ki=1 (βi / (τ pβi + 1)) + 1p ) , βˆk = (βk2 /(τ pβk + 1)) and μ, ˆ κˆ comes from solving the following equations: K −1 1 , Mpβˆi γ + 1 K − 1 i=1,i =k ⎞ ⎛ K K wβˆi wβˆi μˆ + 1/(K − 1) ⎟ ⎜ κˆ ⎝1 + 2 ⎠ = 2 , i=1,i =k Mpβˆi γ i=1,i =k Mwβˆi γ + 1 where γ = 1 − ((K − 1)/M ) + ((K − 1)/M )μˆ .

μˆ =

(7.25)

(7.26)

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7.3 Channel models for massive MIMO In this section, we will provide a brief introduction on the massive MIMO channel models. Due to the application of a large number of antennas, the spatial domain is introduced besides the time and frequency domains, which can be utilized to describe the massive MIMO channel. To this end, two channel models are discussed in this section based on spatially correlation and spatial propagation paths [9,10].

7.3.1 Correlation-based channel model Consider a multiuser massive MIMO system with M -antenna BS serving K singleantenna UEs. The compound downlink channel matrix can be denoted as H = [h1 , h2 , . . . , hK ] ∈ CM ×K , where hk ∈ CM ×1 satisfies hk ∼ CN (0, Rk ). Rk is the channel correlated matrix (CCM) as Rk = E hk hkH .

(7.27)

Therefore, by the Karhunen–Loeve representation, the channel of the kth UE can be represented as 1/2

hk = R k zk ,

(7.28)

where zk ∼ CN (0, IM ). Notice that (7.28) can be utilized as a channel model for capacity analysis or system design in massive MIMO systems, called correlationbased channel model [9]. Moreover, denote the singular value decomposition (SVD) of the CCM as Rk = Uk k UkH , where k = diag λk,1 , λk,2 , . . . , λk,M is a diagonal matrix with ordered eigenvalues. Therefore, the dimensional of the channel can be reduced by only considering the rk dominant singular values. Specifically, the approximation with rk dominate singular values can be represented as (r )

(r )

hk k = Uk k

(r )

k k

1/2

(r )

zk k ,

(7.29)

(r ) (r ) (r ) where Uk k = Uk ( :, 1 : rk ), k k = (1 : rk , 1 : rk ) and zk k ∼ 0, Irk . This approximation, which only considers the dominant rk singular values, can be leveraged to reduce the channel acquisition overhead, especially when the channel sparsity exists, where the approximation with much smaller rk than M can collect most power of the channel.

7.3.2 Spatial channel model Another way to establish the massive MIMO channel model is from propagation perspective. The channel between the BS and the UE can be seen as a linear combination of multiple different propagation paths, which is caused by scatters between the BS and the UE. This model is called spatial channel model, which is also defined in

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3GPP [10], as shown in Figure 7.3. Specifically, we consider an N -antenna UE and an M -antenna BS, and the channel H ∈ CN ×M can be represented as H=

L

H αl ar θlr at θlt ,

(7.30)

l=1

where L is the number of resolvable paths, αl is the path gain of the lth path, θlr , θlt denote angle-of-arrival and angle-of-departure (AoD) of the lth path, respec (AoA) tively. ar θlr and at θlt are the steering vectors or called array response vectors that represent the phase shift difference at different antenna elements caused by the different time delay at the receive/transmit side. Specifically, they can be denoted as 1 r r r T ar θlr = √ 1, e−j2π (d/λ)θl , e−j2π (d/λ)2θl , . . . , e−j2π (d/λ)(N −1)θl . N

(7.31)

1 −j2π (d/λ)θ t −j2π (d/λ)2θ t t T l,e l , . . . , e−j2π (d/λ)(N −1)θl at θlt = √ . 1, e M

(7.32)

Notice that the descriptions (7.31) and (7.32) are for the uniform linear array (ULA) and as for the uniform planar array (UPA); the array response vector can be denoted r ar φlr , θlr and at φlt , θlt , where φlr and φlt are the azimuth AoA and AoD, and θ and l θlt are the elevation AoA and AoD. The form of ar φlr , θlr and at φlt , θlt satisfies 1 r r T ar φlr , θlr = √ 1, e−j2π (d/λ)θl , . . . , e−j2π (d/λ)(N1 −1)θl N r r T ⊗ 1, e−j2π (d/λ)φl , . . . , e−j2π (d/λ)(N2 −1)φl ,

(7.33)

Scatter

θ t1 θ r1

1

θ tl θ rl

BS Scatter

Figure 7.3 Spatial channel model

M UE

Massive MIMO for 5G and beyond 1 −j2π (d/λ)θ t t T l , . . . , e−j2π (d/λ)(M1 −1)θl 1, e ar φlt , θlt = √ M t t T ⊗ 1, e−j2π (d/λ)φl , . . . , e−j2π (d/λ)(M2 −1)φl ,

207

(7.34)

where M1 × M2 = M and N1 × N2 = N . It should be emphasized that due to the limited scatters surrounding the BS, the number of resolvable paths L is usually much smaller than the antenna number M , e.g., L = 6 M = 128 in [11]. This is the channel sparsity in angle-domain of massive MIMO, which can be utilized to reduce the channel estimation and feedback overhead, and will be introduced in detail in the following sections.

7.4 Signal detection for massive MIMO The significant performance improvement of massive MIMO has been introduced in previous sections. However, the large number of antennas and UEs sharply increases the signal detection complexity, channel estimation and channel feedback overhead, which becomes the vital problem in massive MIMO systems. For instance, the complexity of the traditional nonlinear maximum likelihood or sphere decoding detection, which have been proved promising in traditional MIMO systems, scales exponentially with the number of UEs. In massive MIMO systems, tens of UEs are usually served simultaneously, which is much larger than that in traditional MIMO systems where the numbers of UEs are usually 2–8 [7]. Hence, the complexity of maximum likelihood and SD is unacceptable in massive MIMO systems. On the other hand, as discussed in Section 7.2, the low-complexity linear signal detection methods such as MRC, ZF and MMSE can obtain near-optimal performance in massive MIMO systems. However, these methods require large-dimensional matrix inversion operation, which still suffers from huge complexity due to the large number of antennas. Therefore, how to reduce the complexity of matrix inversion in linear signal detection becomes the key point in massive MIMO signal detection. In this section, we will discuss the signal detection in massive MIMO systems, where two typical signal detection methods with simplified matrix inversion are introduced [4,12].

7.4.1 System model and MMSE detection In this section, we first introduce the system model for uplink signal detection in massive MIMO systems and the traditional MMSE-based soft-decision. We consider a massive MIMO system with M -antenna BS, and K single-antenna UEs are served with the same time–frequency resource. The UEs encode their bits sequence and then carry out constellation mapping for transmission. Denote s ∈ CK×1 as the transmitted signal, where s(k) denotes the signal symbol transmitted by the kth UE, e.g., quadrature amplitude modulation (QAM) or quadrature phase shift keying

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symbol. We assume that each UE transmits signals with equal power. Therefore, the uplink received signals at the BS y ∈ CM ×1 can be represented as y = Hs + n,

(7.35)

M ×K

M ×1

where H ∈ C is the channel between UEs and the BS, and n ∈ C denotes the additive white Gaussian noise (AWGN) with zero mean and variance of N0 . To realize MMSE-based soft-decision, two steps should be carried out: (1) detect the transmitted signals; (2) calculate the log-likelihood ratio (LLR) to recover the transmitted bit sequence. Then, we will provide detailed introduction for the above two steps. To detect the transmitted signals, the MMSE signal detection matrix W should be calculated at first as −1 W = HH H + N0 Es−1 I HH . (7.36) Then, the transmitted signal s can be estimated as sˆ = Wy. For simplification, the procedure can be reorganized. Specifically, denoting A = HH H + N0 Es−1 I, we can first calculate A−1 and y¯ = HH y and then calculate sˆ = A−1 y¯ . In this way, the largedimensional matrix multiplication A−1 HH is decomposed into two times of matrix– vector multiplication, which reduces the calculation complexity. However, the above procedure still requires matrix inversion of large-dimensional matrix A. To calculate the LLR, each element of sˆ is assumed to approximately satisfy i.i.d. complex Gaussian distribution. Specifically, sˆk = sˆ(k) is modeled as sˆk = μk sk + ek , where μk is the effective channel gain of the kth UE, ek corresponds to noise-plusinterference (NPI). Denoting vk2 as the variance of ek and b as the label of bits sequence, the LLR of the kth UE’s bth transmitted bit can be denoted as 2 2 sˆk sˆk 2

Lk (b) = ρk min − a − min (7.37) − a , a∈Ob0 μk a ∈Ob1 μk where ρk2 = μ2k /vk2 is effective signal-to-noise ratio (SNR), Ob0 and 1b denote the set of constellation symbols that contains 0 or 1 at the bth bit, respectively. To obtain the effective channel gain μk and the variance of NPI vk2 , sˆk is denoted in the following form based on (7.36) as sˆk = wk Hs + ws n = wk hk sk +

K

wk hi si + wk n,

(7.38)

i=1,i =k

where k = W(k, : ) and hk = H( :, k). According to (7.38), the effective channel gain of the kth UE can be denoted as μk = wk hk and the variance of NPI vk2 can be represented as 2 (7.39) vk2 = E sˆk − E |μk sk |2 = Es μk − Es μ2k . It should be pointed out that to obtain μk and vk2 , first the inversion of A whose complexity is O(K 3 ) should be calculated. Therefore, in massive MIMO systems with tens of UEs, the complexity of signal detection is still too high. Meanwhile, the matrix inversion A−1 requires a large number of division operations, which is difficult

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209

for hardware implementation. Next, we will introduce how to simplify the calculation of A−1 .

7.4.2 Neumann sequence-based signal detection In this section, we will introduce Neumann sequence-based signal detection. The key idea is to realize the matrix inversion by utilizing Neumann sequence, which can reduce the calculation complexity of A−1 . Specifically, A−1 can be expanded by Neumann sequence as A−1 =

∞

n X−1 (X − A) X−1 ,

(7.40)

n=0

n where X should satisfy limn→∞ I − X−1 A = 0. We can decompose A as A = D + E, where D is an diagonal matrix whose diagonal elements are equal to diagonal elements of A, and E is the residual error. Let X = D, (7.40) can be rewritten as A−1 =

∞

n −D−1 E D−1 ,

(7.41)

n=0

n n since limn→∞ I − X−1 A = 0 holds due to limn→∞ −D−1 E = 0. To reduce the complexity of calculating A, we can truncate (7.41) to obtain an approximation of A−1 as ˆ p−1 = A−1 ≈ A

P−1

n −D−1 E D−1 ,

(7.42)

n=0

where P is the number of reserved terms. Equation (7.42) is able to significantly reduce ˆ −1 ≈ the complexity of calculating A−1 when P ≤ 3. For example, when P = 1, A −1 D , which means only the inversion of the diagonal matrix D is calculated with ˆ 2−1 = D−1 − D−1 ED−1 with complexity complexity O (K); when P = 2, we have A 2 O K ; when P = 3, we have ˆ 3−1 = D−1 − D−1 ED−1 + D−1 ED−1 ED−1 , A (7.43) 3 with complexity O K . It should be noticed that although (7.43) has similar complexity in directly calculating A−1 , (7.43) requires much fewer times of division operations since only D−1 needs division operations. Then, we provide the signal detection error analysis of the Neumann series-based signal detection. Specifically, according to the Neumann series approximation shown in (7.42), the approximation error can be denoted as ˆ P−1 =

P = A−1 − A

∞

n −D−1 E D−1

n=P ∞ P

= −D−1 E

n

−D−1 E D−1 = −D−1 E

P

n=0

(7.44) A−1 .

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ˆ P−1 as Then, the transmitted signal can be estimated using A ˆ P−1 HH y = (A − P ) y¯ = sˆ − P y¯ . sˆP = A

(7.45)

According to (7.45), the estimation error of Neumann series-based signal detection can be denoted as (7.46) ˆsP − sˆ22 = P y¯ 22 = −D−1 EP A−1 y¯ 22 ≤ D−1 EPF ˆs22 . From (7.46), we can notice that if the condition D−1 EF < 1

(7.47)

holds, the approximation error approaches zero when K → ∞. Also, (7.46) is a sufficient condition for (7.42) to converge. The following lemma provides the insights that (7.46) is satisfied with high probability for massive MIMO systems with a larger antennas number of BS M than the number of the users K. Lemma 7.3. When M > 4 and the elements of H are i.i.d. complex Gaussian distribution with zero mean and unit variance, then, we have −1 P 2M (M + 1) K2 − K . Pr D EF ≤ α ≥ 1 − 2/P α (M − 1)(M − 2)(M − 3)(M − 4) (7.48) The proof of Lemma 7.3 can be found in [4]. Lemma 7.3 provides the conditions for which the estimation error (7.46) is small. Specifically, when M → ∞ and the number of users K keeps constant, we have Pr D−1 EPF < 1 → 1 for α ∈ (0, 1]. This implies that the Neumann series converges with probability 1 and the estimation error for any P, e.g., P = 1, is arbitrarily small. Thus, by utilizing Neumann series, the complexity of matrix inversion can be significantly reduced in massive MIMO systems with large M . Then, we analyze how to calculate LLR after exploiting Neumann series to calˆ P−1 into A−1 , the effective channel gain μˆ P,k culate matrix inversion. By substituting A 2 ˆ P−1 = A ˆ P−1 HH , similarly to and the variance of NPI vˆ P,k are related to P. Defining W (7.38) and (7.39), we can obtain ˆ P,k hk , μˆP,k = w 2 2 vˆ P,k = Es aˆ P,k AGaˆ P,k − Es μˆ 2P,k ,

(7.49)

ˆ P−1 (k, : ), aˆ P,k = A ˆ P−1 ( :, k) and G = HH H. For reducing the comwhere w ˆ P,k = W 2 2 plexity, we set P = 1 and use vˆ 1,k to replace vˆ P,k . Then, we have 2 vˆ 1,k = Es dk−2 akH gk − Es μˆ 21,k ,

(7.50)

where dk = D(k, k), ak = A( :, k) and gk = G( :, k). As analyzed before, when the antennas’ number of BS M is large, P = 1 can also guarantee low-approximation error and estimation error.

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211

7.4.3 Iteration-based signal detection In this subsection, we discuss iteration-based signal detection. Its key idea is combining the matrix inversion and signal detection, and formulating the problem as solving high-dimensional linear equations. Then, traditional numerical iteration methods can be utilized. By this way, the received signals are directly detected without matrix inversion, which reduces the calculation complexity in massive MIMO systems. Specifically, MMSE signal detection sˆ = A−1 y¯ can be converted to linear equations Aˆs = y¯ . Then, the conjugate symmetric matrix A = HH H + N0 Es−1 I can be decomposed as A = D + L + LH ,

(7.51)

where D, L and LH denote diagonal element matrix, strictly upper triangular matrix and strictly lower triangular matrix of A, respectively. Based on (7.51), the traditional numerical iteration methods can be utilized to solve sˆ from Aˆs = y¯ . For instance, when Gauss–Seidel method is exploited, we have sˆ(i) = (D + L)−1 y¯ − LH sˆ(i−1) , i = 1, 2, . . . (7.52) where i denotes the iteration number and sˆ(0) denotes the initial solution. It should be emphasized that D + F in (7.52) is lower triangular matrix, which can be inverted with much lower complexity compared to that of arbitrary matrix. Thus, the complexity of signal detection can be sharply reduced. Moreover, other iteration methods can also be utilized to solve Aˆs = y¯ , such as Jacobi method and Richardson method. In this subsection, we take Gauss–Seidel method for an example. As discussed above, two aspects mainly affect the performance of iteration-based signal detection, which are convergence and initial solution. The following lemma analyzes the convergence of Gauss–Seidel method. Lemma 7.4. For uplink massive MIMO systems, Gauss–Seidel method-based signal detection converges with probability 1. Proof: Define B = − (D + L)−1 LH and f = (D + L)−1 y¯ , where B is called iteration matrix. Then, (7.52) can be represented as sˆ(i+1) = Bˆs(i) + f .

(7.53)

For arbitrary initial solution, if we have limi→∞ sˆ(i) = sˆ and sˆ = Bˆs + f , then we say the iteration converges. The sufficient and necessary condition of (7.53) is that the spectral radius of the iteration matrix B satisfies ρ(B) = max |λk | < 1, 1≤k≤K

(7.54)

where λk is the kth eigenvalue of B. Based on the definition of eigenvalue, we have Brk = − (L + D)−1 LH rk = λk rk ,

(7.55)

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where rk denotes the eigenvector corresponding to the kth eigenvalue. Equation (7.55) can be rewritten as (7.56) LH r = λk (L + D) r = LH − A λk r. Then, we have (λk − 1) rH LH r = λk rH Ar.

(7.57)

Considering that matrix A = HH H + N0 Es−1 I is symmetric, we have (λk − 1) rH L + LH r = 2λk rH Ar. (1 − λk ) r Dr = (1 + λk ) r Ar. H

H

(7.58) (7.59)

Notice that in massive MIMO systems, each element of H follows i.i.d. complex Gaussian distribution. Therefore, the detection matrix A is a conjugate symmetric and positive definite matrix, which means rH Dr > 0 and rH Ar > 0. Then, based on (7.59), we have (1 − λk )(1 + λk ) > 0, which means |λk | < 1. Thus, according to (7.54), the Gauss–Seidel method-based signal detection converges with probability 1. Then, how to select initial solution is another question. For traditional Gauss– Seidel method, the initial solution is usually set as zero vector, due to the lack of prior information. This may increase the number of iteration since the initial solution is far away from the optimal solution. Hence, to speed up convergence and further reduce the complexity of Gauss–Seidel method, many works have proposed initial solution selection schemes. For instance, in [12], the prior information obtained from the received signal is utilized to find a suboptimal initial solution, by which the convergence of Gauss–Seidel method is substantially accelerated. As discussed above, the complexity of Gauss–Seidel method-based signal detection in (7.52) is O(K 2 ). Figure 7.4 provides the complexity comparison between iteration-based signal detection (using Gauss–Seidel method), Neumann seriesbased signal detection and accurate matrix inversion-based MMSE detection (using Cholesky decomposition). We can observe from Figure 7.4 that when i ≤ 3, the Neumann series-based signal detection has lower complexity than that of accurate matrix inversion-based MMSE detection. However for iteration-based signal detection, the complexity is always O(K 2 ) no matter what is the value of i. Therefore, iteration-based signal detection is more effective to reduce the complexity, especially when the user number K is large. Finally, we provide the bit error rate (BER) performance comparison of signal detection methods between iteration-based signal detection (using Gauss–Seidel method), Neumann series-based signal detection and accurate matrix inversion-based MMSE detection in Figure 7.5. The UEs transmit signals with 64QAM modulation and 1/2 convolution code. For the BS, we calculate LLR and then utilize the soft-decisionbased Viterbi decoder to decode the transmitted signals. The channel between the BS and the UEs is Rayleigh fading with M = 128 and K = 16. We can observe that as the iteration number or number of approximation terms increases, both iteration-based

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213

× 104 Neumann-based algorithm, i = 2 Neumann-based algorithm, i = 3 Neumann-based algorithm, i = 4 GS-based algorithm, i = 2 GS-based algorithm, i = 3 GS-based algorithm, i = 4 MMSE with Cholesky decomposition

6

Number of complex multiplications

5

4

3

2

1

2

10

15

20

25

30

32

Number of users

Figure 7.4 The complexity comparison between different signal detection methods

signal detection and Neumann series-based signal detection obtain lower BER performance, and they can approach the BER performance of optimal MMSE detection when i = 4. However, for equal i, iteration-based signal detection achieves better BER performance than that of Neumann series-based signal detection, which means it can obtain better BER performance with lower complexity.

7.5 CSI acquisition for massive MIMO In the previous section, we introduce the signal detection methods in massive MIMO systems. However to detect the transmission signals, CSI is essential. Moreover, the downlink CSI is necessary for many channel adaptive technologies at the BS such as power allocation and precoding, which can bring performance improvement in massive MIMO systems [13]. In traditional systems, CSI is usually acquired by channel estimation and channel feedback schemes. However, the traditional channel

214

Flexible and cognitive radio access technologies for 5G and beyond 100

10−1

10−2

BER

10−3

10−4

Neumann-based algorithm, i = 2 Neumann-based algorithm, i = 3 Neumann-based algorithm, i = 4 GS-based algorithm, i = 2 GS-based algorithm, i = 3 GS-based algorithm, i = 4 MMSE algorithm

10−5

10−6

10−7

9

10

11

12

13

14

15

16

SNR (dB)

Figure 7.5 The complexity comparison between different signal detection methods

estimation and channel feedback schemes suffer from unacceptable training and feedback overhead [5,14], due to the large antenna number in massive MIMO systems, which makes CSI acquisition one of the critical challenges for practical application of massive MIMO. In this section, we introduce the CSI acquisition schemes in massive MIMO systems, including channel estimation and channel feedback schemes.

7.5.1 Channel estimation for massive MIMO Channel estimation is to obtain the channel information by known training signals. For the conventional orthogonal training schemes, the length of the training pilots should be at least the number of antennas M . Therefore, when the number of antennas is large in massive MIMO systems, the training overhead becomes a heavy burden for practice concern. To reduce the training overhead of channel estimation, the low-rank property of the massive MIMO is widely utilized, e.g., channel sparsity in angledomain and low-rank property of channel correlation matrix [5,15,16]. By exploiting the low-rank property, the effective dimension of the channel can be reduced, which correspondingly reduces the training overhead. Then, we will introduce channel

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215

sparsity-based channel estimation and channel correlation-based channel estimation in detail.

7.5.1.1 Channel sparsity-based channel estimation Although the channel of massive MIMO is high dimensional, many researches have shown the intrinsic sparsity in the massive MIMO propagation. Due to the limited scatters, the massive MIMO channel enjoys sparsity in angle-domain, which can be reflected through a discrete Fourier transform (DFT) transformation on the frequency-domain channel. This angle-domain sparsity has been widely utilized in channel estimation for massive MIMO systems [5,15]. Specifically, compressive sensing algorithms can be applied to recover the high-dimensional channel with a very few training pilots, due to the angle-domain sparsity. In this subsection, a distributed compressive sensing-based channel estimation scheme is introduced [15] to give a glance on channel sparsity-based channel estimation for massive MIMO systems. We consider a frequency division duplexing (FDD) massive MIMO system with M -antenna BS serving K N -antenna UEs. The BS uses T time slots to transmit pilots to the UEs. Denote the pilot signal at the jth time slot as xj ∈ CM ×1 . Then, the ith UE’s received signal at the jth time slot yij ∈ CN ×1 can be denoted as yij = Hi xj + nij ,

(7.60)

where Hi ∈ CM ×M is the channel between BS and the ith UE, and nij ∈ CN ×1 denotes AWGN with CN(0, 1). Denote X = [x1 , . . . , xT ], Y = [yi1 , . . . , yiT ] and Ni = [ni1 , . . . , niT ]. Then, (7.60) can be reorganized as (7.61) Yi = Hi X + Ni , H where tr XX = PT is the total transmission power over the T time slots and P denotes the transmission power of each time slot. For FDD mode, the downlink CSI acquisition contains two steps: (1) each UE estimates their channel Hi independently; (2) UEs feedback the estimated channel ˆ i can be denoted as ˆ i . If adopting traditional least squares estimate (LSE) method, H H ˆ i = Yi X† , H (7.62) −1 where X† = XH HHH . However, LSE requires T ≥ M . This induces high training overhead in massive MIMO systems with a large number of antennas M . To reduce the training overhead, the channel angle-domain sparsity has been utilized [5,15]. Many channel measurements have shown that due to limited scatters at the BS side, the channel of massive MIMO enjoys sparsity in angle-domain. In [11], the angle-domain channel (or called virtual channel) is constructed through DFT. Specifically, the channel Hi can be represented as Hi = AR Hiw ATH ,

(7.63)

where AR ∈ CN ×N and AT ∈ CM ×M denote the DFT matrix to perform the angledomain conversion at the UE side and the BS side, respectively. Hiw ∈ CN ×M denotes the angle-domain channel (or virtual channel). When H(p, q) is nonzero, it means

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that there is a physical propagation path between the pth BS AoD in AR and the qth UE AoA in AT corresponding to several scatters. Field channel measurements reveal that in massive MIMO systems, the number of scatters at the BS side is much smaller w than the number of antennas, which means the angle-domain channel Hi has sparsity. Specifically, denoting supp hij as the set of the nonzero elements’ index (or called sparsity support), we have the following observations: ●

Individual joint sparsity due to limited scattering at the BS side: Due to limited scattering at the BS side and relatively rich scattering at the UEs side, the massive MIMO channel is usually sparse at the BS side but not at the UEs side. Specifically, the row vectors within Hiw usually have the same sparsity support, as shown in Figure 7.6, i.e., there exists an index set i , 0 < |i | M , such that supp (hi1 ) = supp (hi2 ) = · · · = supp (hiN ) = i ,

(7.64)

Hiw .

●

where hij denotes the jth row of Distributed joint sparsity due to common scattering at the BS side: The channel matrices of different UEs are usually correlated. Specifically, the UEs tend to share some common scatters at the BS side, as shown in Figure 7.6, there exists an index set c satisfying ∩Ki=1 i = c .

(7.65)

Furthermore, the elements of :, i ) are i.i.d. complex Gaussian distribution with zero mean and unit variance. Hiw (

To reduce the training overhead of downlink CSI acquisition in massive MIMO systems, the joint sparsity introduced above can be utilized. A distributed compressive CSI estimation and a feedback frame are proposed in [15]. This frame fully exploits the joint sparsity within and between different UE channels, which can significantly reduce the training overhead. Specifically, the frame contains the following three steps: (1) pilot training: the BS sends training pilots X ∈ CM ×T with T M ; Rich local scatterers for MS

Local scatterers at BS for MS 1

Ω1

Hω1 = MS 1 Ωc

Shared common scatterers for MS 1, 2

BS

Hω2 = Local scatterers at BS for MS 2

MS 2

Ω2 Nonzero coefficients Zero coefficients

Figure 7.6 The distributed joint sparsity of massive MIMO channel

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217

(2) compressive observation and feedback: the ith UE observes the compressive measurements Yi from the received pilots symbols and feeds back to the BS; (3) joint CSI recovery at the BS: the BS recovers the CSI H1w , . . . , HKw based on the observation {Y1 , . . . , YK }. Hence, the problem can be formulated as min {Hi }

i=K

Yi − Hi X2F ,

s.t. Hiw satisfies (7.64) and (7.65).

(7.66)

i=1

To solve (7.66), we first define the following parameters: M H M H T ×N ¯ ¯ Y AR ∈ C , X= Yi = X AT ∈ CT ×M , PT i PT w H M ×N ¯ i = Hi ¯ i = M NiH AR ∈ CT ×N . H ∈C , N PT Substituting (7.67) into (7.61), we have ¯H ¯i +N ¯ i. ¯i = X Y

(7.67)

(7.68)

¯ is the obserEquation (7.68) is a standard vector recovery problem, where X sparse ¯ i is the sparse matrix as discussed in (7.64) ¯ H and H vation matrix satisfying tr X and (7.65). Further, we have ¯ i X ¯ 2F = M Yi − Hi X2F . ¯i −H (7.69) Y PT ˆ i subject to the joint sparsity in (7.64) Therefore, (7.66) is converted to find H K ¯i −H ¯ i X ¯ 2F . Based on the above transformation, a and (7.65) to minimize i=1 Y joint orthogonal match pursuit (JOMP) algorithm is proposed to recover the sparse channel from compressive observations, where the joint sparsity of the massive MIMO channels are fully utilized. Let A be the matrix composed of rows of A indexed by the elements in and A be the matrix composed of columns of A indexed by the elements in . Then, the procedure of JOMP is illustrated in Algorithm 1. In Algorithm 1, η1 and η2 are thresholds, and S denotes prior information of sparsity support. In step 2, the common support between different UEs is utilized to give a joint estimation on common support. Then, in step 3, the joint sparsity between different UE antennas is exploited to estimate the individual support. Finally, in step 4, the nonzero elements of the channel are estimated based on LSE. In Figure 7.7, we provide the performance comparison between JOMP and other compressive sensing algorithms. The parameters are M = 160, K = 40, N = 2, sc = 9, s = 17 and P = 28 dB. We can observe from Figure 7.7 that all the algorithms obtain better normalized mean squared error (NMSE) performance as the training overhead T increases. Specifically, with equal T , JOMP can obtain lower NMSE than all the other algorithms and can achieve near-optimal performance with T = 50, which is much smaller than antenna number M = 160. This is because JOMP makes fully use of the joint sparsity between and within UE channels.

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Algorithm 1: JOMP Require: {Yi }, X, S = {sc , {si }}, η1 , η2 (η1 < 1,η2 > 1). Ensure: The estimation of {Hi }, Hie . ¯ i and X ¯ based on (7.67). 1: (Initialization) Calculate Y ¯ i , ec = ∅ and repeat the 2: (Common support identification) Initialize Ri = Y following steps sc times.

¯ HR . —A: Estimate the remaining index set as i = arg max||=si −|ec | X i F

H 2 ¯ : ) Ri F ≥ η1 N . —B: Prune support i by i = j : j ∈ i , X(j, —C: Update the estimated common support ec = ec ∪ arg maxj Ki=1 I{j∈ } . i ¯ e X ¯ i , where Pe is a projection matrix as Pe = X ¯ † e . —D: Ri = I − Pec Y c c c c 3: (Individual support identification) Set ei = ec and estimate the individual support ei for each UE i individually. Stop if Ri 2F ≤ (η2 NM )/P or the following steps repeated (si − sc ) times. —A: Update the estimated individual support as ei = ei ∪ ¯ : )H Ri F . arg maxj X(j, ¯i . —B: Ri = I − Pei Y 4: (Channel estimation by LSE) The estimated channel for UE i is Hie = ¯ ie )H ATH , where H ¯ ie is (H ¯ ie )ei = X ¯ † eY ¯ e [M ]/ ei = 0. ¯ AR (H i i , ( H i ) e 5: return Hi .

It should be emphasized that the compressive sensing-based channel estimation schemes such as JOMP may face power leakage problem. Specifically, when the channels AoA and AoD are not located on the grid that is sampled by the DFT matrix, the angle-domain channel in (7.63) may be approximately sparse but not exactly sparse, i.e., few elements of Hiw have large values and most elements have quite small values but not zero. Under this circumstance, compressive sensing algorithms suffer from performance decline since part of the power of Hiw on these small-value elements cannot be detected as a support. Since in practical scenario, the AoAs/AoDs are distributed continuously and the number of antennas M is limited, the power leakage problem tends to happen. To solve this problem, many parametric estimation algorithms that directly estimate AoAs/AoDs and corresponding path gains are proposed. These schemes, widely called super-resolution channel estimation, also utilize the channel sparsity on angle-domain to reduce the training overhead. The readers can find these super-resolution channel estimation schemes in [17].

7.5.1.2 Channel correlation-based channel estimation In this subsection, an open- and close-loop channel estimation method based on the channel correlation is introduced [16]. We consider massive multiple-input single output (MISO) system with Nt -antenna BS and a single-antenna UE. We assume that

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219

NMSE of CSIT versus T

100

LS

OMP SD-OMP 10−1 NMSE of CSIT

2-norm SOMP M-BP J-OMP 10−2

10−3 Genie-aided LS 30

35

40

45

50

55

60

65

70

CSIT estimation overhead T

Figure 7.7 NMSE performance of distributed compressive sensing-based channel estimation scheme

the coherence time lasts L time slots, which is defined as a block. Then, the received signal at the lth time slot of the ith block can be denoted as yi [l] = hiH xi [l] + ni [l] ,

(7.70)

where yi [l] is the received signal, hi ∈ CNt ×1 is the channel vector, xi [l] ∈ CNt ×1 is the transmitted signal satisfying E x [l] 2 = ρ and ni ∼ CN(0, 1). Each block contains a training stage and a transmission stage. We assume that T < L, T < Nt time slots are utilized for channel training. Then, for the training pilots, the received signal yi,train = [yi [0] , . . . , yi [T − 1]] can be represented as yi,train = XiH hi + ni,train ,

(7.71)

where Xi = [xi [i] , . . . , xi [T − 1]] is the transmitted pilot, and the noise integrated is ni,train = [ni [0] , . . . , ni [T − 1]]. We assume that each pilot has equal power as Xi ∈ X = F : F ∈ CNt ×T , FH F = ρIT . For the channel, we adopt the time-spatial correlation model and assume that hi obeys Gauss–Markov distribution as h0 = R 1/2 g0 , hi = ηhi−1 + 1 − η2 R 1/2 gi , i ≥ 1, (7.72) H where R = E hi hi is the spatial correlation matrix, which is related to UE’s position and can be assumed unchanged during the L time slots, gi is the time-domain update

220

Flexible and cognitive radio access technologies for 5G and beyond whose elements satisfies i.i.d. CN 0, INt and η is the time correlation coefficient. Notice that R is a positive-definite matrix, which can de decomposed as UUH , where U is unitary and is a diagonal matrix with descending eigenvalues λi , i = 1, 2, . . . , Nt . We first introduce an open-loop channel estimation method, in which no information is required to be fed back from the UE to the BS. Specially, we assume the BS and the UE share common training pilots set, i.e., a B-bit quantization codebook P = {P1 , . . . , P2B }. Then, the training pilots at the ith block can be defined circularly as Xi = Pmod(i,2B )+1 ,

i = 0, 1, . . . , T − 1.

(7.73)

Then, for the channel estimation, we notice that hi is not only correlated to yi,train , but i−1 also correlated to yk,train k=0 and the channel statistic property R and η. And (7.72) can be seen as the status evolution. Hence, the Kalman filter can be utilized to track the channel evolution and give the estimation of the channel. Specifically, we can define i2 hˆ i1 |i2 = E hi1 | yk,train k=0 (7.74) as the estimated value of hi1 , where i1 ≥ i2 . Then, the sequential MMSE estimator can be utilized to estimate hˆ i|i as the final estimation of hi , which is shown in Algorithm 2. Then, we will introduce a memory-based close-loop channel estimation scheme. The procedure is shown in Figure 7.8. Specifically, the training pilots at the ith block i−1 can be selected based on the previous training pilots yk,train k=0 . Therefore, we let the UE select the best training pilots Pi,best based on the predefined codebook P = {P1 , . . . , P2B }. Then, it feeds back Pi,best to the BS, and the BS transmits Xi = Pi,best as training pilots. Finally, the UE tracks the channel based on Algorithm 2. How to select best training pilots Pi,best is the key point. Specifically, Pi,best can be picked out by minimizing the MSE of channel estimation. The MSE between hi and hˆ i|i can be denoted as 1 1 1 MSE {Xi } = E hi − hˆ i|i 22 = tr Ri|i = tr Ri|i−1 − Rp,i , (7.75) Nt Nt Nt

Algorithm 2: Open-loop channel estimation method 1: Initialization: h0|−1 = 0, R0|−1 = R = E h0 h0H . ˆ i|i−1 = ηhˆ i−1|i−1 . 2: Prediction: h 3: Minimum prediction MSE matrix: Ri|i−1 = η2 Ri−1|i−1 + 1 − η2 R. −1 4: Kalman filter matrix: Ki = Ri|i−1 Xi IT + XiH Ri|i−1 Xi . ˆ i|i−1 + K yi,train − Xi hˆ i|i−1 . 5: Modification: hˆi|i = h 6: Update: Ri|i = INt − Ki XiH Ri|i−1 .

Massive MIMO for 5G and beyond

hi−1

Хi−1

Transmitter

Track channel statistics

Receiver

yi−1, train

221

Select the best training signal from Р for Хi Feedback channel

Figure 7.8 Procedure of close-loop channel estimation 12

a = [0.6, 0.9]

Normalized average received SNR (dB)

Normalized average received SNR (dB)

12 11

10

10

9 8 7 OL/SS, T = 16 OL/SS, T = 1 OL w/ memory OL w/ memory, SNR-based CL w/ memory, MSE-based

6 5 4 0

a = [0.6, 0.9]

11

1

2

3 4 5 6 Fading block index i

7

8

9 8

6 5 0

9

(a)

OL/SS, T = 16 OL/SS, T = 2 OL w/ memory OL w/ memory, SNR-based CL w/ memory, MSE-based

7

1

2

3

4 5 6 7 Fading block index i

8

9

(b)

Figure 7.9 Procedure of close-loop channel estimation: (a) ρ = 0 dB, T = 1 and (b) ρ = 0 dB, T = 2 where Rp,i is the variance of hˆ i|i as −1 H Xi Ri|i−1 . Rp,i = Ri|i−1 Xi IT + XiH Ri|i−1 Xi

(7.76)

Then, the training pilots at the ith block Pi,best can be denoted as Pi,best = arg min MSE (Pk ) = arg max tr Rp,i .

(7.77)

Pk ∈P

Pk ∈P

Notice that there exits other criterion to choose Pi,best , such as maximizing the normalized average received SNR, which can be found in [16]. Figure 7.9 illustrates the performance of the open- and close-loop channel estimation scheme. The parameters are Nt = 64 or Nt = 16, L = 10, T = 2, ρ = 0 dB, α = 0.9 and B = 6 bits. We can observe that with equal T , the open- and close-loop scheme outperform the traditional scheme. Moreover, we can notice that the close-loop scheme can achieve better performance than the open-loop scheme. This indicates that by utilizing the spatial correlation and time correlation, the channel estimation overhead can be significantly decreased.

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7.5.2 Channel feedback for massive MIMO In FDD massive MIMO systems, since the channel reciprocity does not hold, the UE should feed the estimated downlink channel back to the BS through uplink channel to realize channel adaptive techniques such as precoding or power allocation. This procedure is called channel feedback. Since the channel-adaptive techniques is important to fulfill the spectrum efficiency gain of massive MIMO, channel feedback is essential in FDD massive MIMO systems. Channel feedback is usually realized by limited feedback [18]. Specifically, the UE first quantizes the estimated downlink channel by a codebook that is known at both the UE side and the BS side and then feeds the index back to the BS. However, the required size of existing channel feedback codebooks increases linearly with the number of antennas. For instance, if the number of antennas is 128, the size of the codebook will be 2128 . This large size together with the corresponding large feedback overhead is obviously unacceptable for practical systems. Therefore, investigating small-size codebooks and low-overhead channel feedback schemes becomes one of the vital challenges in massive MIMO systems. In this section, we will introduce representative channel feedback schemes for massive MIMO, including channel sparsity-based channel feedback, channel correlation-based channel feedback and channel partial reciprocity-based channel feedback.

7.5.2.1 Channel sparsity-based channel feedback In this subsection, we will introduce the channel sparsity-based channel feedback schemes. First, we introduce the angle-domain sparsity-based channel feedback scheme [14,19,20]. We consider a massive MIMO system with NT -antenna BS serving an NR -antenna UE, as discussed in Section 7.3.2, the massive MIMO multipath channel can be denoted as H=

P

αi aR θR,i aTH θT ,i ,

(7.78)

1

where P is the path number, aR θR,i and aT θT ,i denote the receive steering vector and transmit steering vector of ith path, respectively, with AoA θR,i and AoD θT ,i , as illustrated in Section 7.3.2. The multipath channel can be converted to angle-domain channel by DFT matrix. Specifically, the angle-domain channel Hb can be denoted as Hb = URH HUT ,

(7.79)

where UR and UT are DFT matrix for the UE side and the BS side, respectively. The massive MIMO channel enjoys angle-domain sparsity due to limited scatters, e.g., P = 3 ∼ 6 NT [11]. This sparsity can be utilized to reduce feedback overhead with compressive sensing algorithms. Specifically, the angle-domain channel matrix Hb can be vectorized as s = vec{Hb } = (UT ⊗ UR ) vec{H} = H h,

(7.80)

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223

where s is the sparse angle-domain vectorized channel. Notice that the highdimensional vector s can be recovered from a low-dimensional compressive observation by compressive sensing algorithms. Denoting as random Gaussian matrix, the channel vector s can be recovered from the low-dimensional observation y as y = h = s.

(7.81)

Based on the above thought, the angle-domain sparsity-based channel feedback can be described as the following procedures [14]: after the UE estimates the downlink channel h, it compresses h to y as (7.81). Then, the UE feeds the low-dimensional observation y back to the BS. After that, the BS recovers the channel vector sˆ by compressive sensing algorithms. Finally, the BS obtains the downlink channel by hˆ = ˆs. We can notice that by utilizing angle-domain sparsity, only a low-dimensional vector y is quantized and fed back, which reduces the codebook size and feedback overhead. Figure 7.10 illustrates the performance of the angle-domain sparsity-based channel feedback. The parameters are NT = 32, NR = 32 and P = 3, the length of y is 8. We can observe from Figure 7.10 that the angle-sparsity-based channel feedback scheme outperforms the conventional random vector quantization (RVQ) and Grassmannian codebook with reduced channel feedback overhead. 70 Full CSIT-optimal ZFBF with full CSIT CS-LF scheme CS-LF lower bound Grassmannian BF Conventional RVQ

60

Sum rate (bps/Hz)

50

40

30

20

10

0 −5

0

5

10

15

20

25

30

Receive power (dB)

Figure 7.10 Sum rate performance of angle-domain sparsity-based channel feedback

35

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3 2 1 0 10

20

30

40

50

60

Figure 7.11 Delay-domain sparsity

Besides the angle-domain channel sparsity, the delay-domain sparsity can also be utilized to reduce the channel feedback overhead. We consider a massive MIMO system with M -antenna BS serving a single-antenna UE. Due to limited scatters, the massive MIMO channel shows sparsity in delay domain, i.e., most power of the channel impulse response (CIR) between the ith BS antenna and the user hi ∈ CL×1 focuses on few elements, where L is determined by the maximum time delay. This sparsity is shown in Figure 7.11. Similar to angle-domain sparsity, compressive sensing algorithms can also be exploited to recover the sparse CIR from a low-dimensional observation. With a similar channel feedback frame, a low-dimensional vector that feeds back to BS is sufficient, which can also reduce the channel feedback overhead. Furthermore, since the time delays of different antennas are very close (much smaller than the sampling interval), the CIRs of different BS antennas can be seen to have common sparsity support. This structured sparsity is utilized in [20] to further reduce the channel feedback overhead.

7.5.2.2 Channel correlation-based channel feedback The channel feedback overhead and codebook size can be reduced by utilizing channel sparsity, since the effective dimension of the channel is much smaller than the number of antennas. On the other hand, utilizing the channel correlation can achieve similar effect. In this section, we will introduce channel feedback schemes based on channel correlation for massive MIMO systems [18,21]. We first introduce a channel feedback scheme based on the correlation between different antennas [21]. We consider a massive MIMO system with M -antenna BS serving K UEs. Considering flat fading channel, the received signal at the nth symbol period for the kth UE can be denoted as yk [n] = hkH wk sk [n] + hkH (7.82) wj sj [n] + zk [n] , j =k

where hk ∈ CM ×1 is the channel between the BS and the kth UE, wk denotes the precoding vector and sk [n] is the transmission signal for the kth UE. As the channels between different BS antennas have correlation, the channel vector can be denoted as 1/2

hk = Rt,k hw,k ,

(7.83)

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225

where Rt,k is the correlation matrix and the elements in hw,k satisfy i.i.d. complex Gaussian distribution. Denoting y [n] = [y1 [n] , y2 [n] , . . . , yK [n]]T , H = [h1 , h2 , . . . , hK ]H , we have y [n] = Hx [n] = HWs [n] + z [n] .

(7.84)

In traditional limitedfeedback, the channel feedback is realized by a quanti zation codebook as C = ck,1 , ck,2 , . . . , ck,2B . The UE first quantizes the estimated normalized channel h¯ k by maximizing inner product as 2 hˆ k = arg max h¯ kH ck .

(7.85)

ck ∈Ck

Then, the index of hˆ k is fed back to the BS. Usually, to guarantee the achievable rate loss caused by the quantization error, the feedback overhead B should increase linearly with the number of antennas M , which may induce unacceptable feedback overhead in massive MIMO systems. To solve this problem, an antenna grouping-based channel feedback scheme is proposed. The key idea of this scheme is that since the channels between different antennas are correlated, the antennas can be separated into groups in which the antennas are strongly correlated. Then, one antenna can be seen as the representative of the channels of its antenna group. Therefore, for one antenna group, feeding back the channel of one antenna is sufficient to recover the full channel for BS due to the strong correlation. With the above thought, the antenna grouping-based channel feedback scheme includes the following steps, as shown in Figure 7.12. First, based on the antenna group pattern that is defined in advance, we can obtain Np = 2Bp effective channels, where Bp is the channel feedback overhead for the antenna group. Specifically, for the ith antenna group pattern, the effective channel gi ∈ CNg ×1 can be denoted as gi = Gi h,

(7.86)

Base station s1 s2

sk

User k

1 Antenna group pattern expansion

Beamforming W

2 x Nt−1

~ (i) hr

i*

H yk

Antenna h(i) group pattern r mapping

Nt

Pattern index (Bp bits/user)

Pattern selection

^ (i)

Antenna group pattern expansion

hr Vector quantization i*

c*

Codeword index (B−Bp bits/user)

Figure 7.12 Antenna grouping-based channel feedback scheme

226

Flexible and cognitive radio access technologies for 5G and beyond

where Gi ∈ CNg ×M is the mapping matrix, which makes the gi (j) as the mean value of the jth antenna group in the ith antenna group pattern. Then, a codebook C of size 2B−Bp is utilized to quantize normalized g¯ i to vi as 2 H (7.87) vi = arg max g¯ i c . c∈C

Finally, the user chooses the best antenna group pattern indexed by p∗ and feeds back ∗ the best quantized codeword vP to the BS. Specifically, to evaluate different patterns, vi is expanded by a matrix Ei ∈ CM ×Np , which expands vi (j) to each antennas in the jth antenna group in the ith antenna group pattern by v˜ i = Ei vi . Therefore, the best antenna group pattern can be selected as 2 (7.88) p∗ = arg max h¯ H v˜ i . i=1,2,...,Np

Figure 7.13 shows the sum rate performance by precoding using the channel feedback by different channel feedback schemes where M = 32, K = 4 and Ng = 16. We can 24

22

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Sum rate (pbs/Hz)

18

16

14

12

10 Perfect CSIT AGB algorithm (B = 64) Conventional vector quantization (B = 64) AGB algorithm (B = 32) Conventional vector quantization (B = 32)

8

6

0

5

10 SNR (dB)

15

20

Figure 7.13 Performance of antenna group-based channel feedback scheme

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227

observe that the antenna group-based channel feedback scheme can achieve a higher sum rate with equal channel feedback overhead. Then, we will introduce a channel feedback scheme based on channel spatial correlation [18]. We consider a massive MIMO system with M -antenna BS and K single-antenna UEs. Assuming that the BS employs ULA and adopting widely utilized spatial channel model, the downlink channel of the kth UE hk ∈ CM ×1 can be denoted as hk =

Pk

gk,i a φk,i ,

(7.89)

i=1

where Pk is the path number between the BS and the kth UE, gk,i is the path gain of the ith path for the kth UE ψk,i is the AoD of the ith path for the kth UE and a(ψ Denoting Ak = k,i ) denotes same form as (7.33). the steering vector with the a φk,1 , a φk,2 , . . . , a φk,Pk and gk = gk,1 , gk,2 , . . . , gk,Pk , the channel can be represented as hk = Ak gk .

(7.90)

We can observe from (7.90) that the channel is decided by the AoDs and the path gains. Since the channel AoDs are mainly determined by the limited scatters surrounding the BS that cannot change their positions frequently, the AoDs can remain unchanged for a relatively long time. On the other hand, the path gains, which is mainly determined by the complex environment surrounding the UEs, will vary in a very short-time period. Therefore, we can conclude that the angle coherence time during which the AoDs remain unchanged is much longer than the channel coherence time. Utilizing this time-domain correlation of channel AoDs, a channel subspace codebook is designed, which can reduce the codebook size and channel feedback overhead. Specifically, we can observe from (7.90) that when channel AoDs remain unchanged, the channel hk is located in a space that is spanned by a φk,i , i = 1, 2, . . . , Pk . Due to the limited scatters, the number of paths Pk is usually much smaller than the number of antennas (P = 2 ∼ 8 [11]). Therefore, the space spanned by a φk,i , i = 1, 2, . . . , Pk is a subspace of the M -dimension space, which is defined as a channel subspace. Based on the channel subspace, an adaptive channel subspace codebook can be designed, where the codewords are distributed in the channel subspace, as shown in Figure 7.14. Specifically, assuming that the BS and the kth UE have already obtained Pk the channel AoDs φk,i i=1 , the BS and the UE can generate the matrix Ak . Then, sub sub sub the codewords in channel subspace codebook Cksub = ck,1 , ck,2 , . . . , ck,2 can be B described as sub = Ak wi , ck,i

(7.91)

where wi ∈ CPk ×1 is a normalized vector that is chosen from a RVQ codebook. Notice that by utilizing channel subspace, the dimension of the quantized vector space is reduced from M to Pk , which means that smaller codebook size is sufficient to achieve

228

Flexible and cognitive radio access technologies for 5G and beyond Channel subspace (P-dimensional, P |hU −1 |2 }, given that −1 P = Ui=0, Pi [2,6]. In other words, higher power (Pi > Pi−1 ) is assigned to the far UEs with weaker channel condition compared with the near UEs with stronger channel condition (|hi−1 |2 > |hi |2 ) to achieve a fair system that satisfies the requirements of all the UEs. Later, the signals of the UEs are superimposed by BS and transmitted

Non-orthogonal radio access technologies

463

Power

Power Freq. Time Detect far UE

Freq.

Power

Time

Subtract far UE

Detect near UE

Freq. Time

Figure 15.1 A scenario of downlink PD-NOMA with U = 2, and SIC is performed by the near UE

over the same resources. The received signal by ith UE is mathematically expressed as follows ⎛ ⎞ ⎜ ⎟ U −1 ⎜ ⎟ ⎜ yi = hi ⎜ Pi xi + P u xu ⎟ ⎟ + wi , ⎝

⎠ u=0, u=i desired signal

(15.1)

IUI

where wi denotes additive white Gaussian noise with CN (0, σw2i ). At the receiver, different from OMA schemes where the correct detection of only desired signal (xi ) is important, the knowledge of IUI stemming from the far UEs with stronger power coefficients {xU −1 , . . . , xi+1 } is required to detect the desired UE. Hence, SIC is performed by the near UE with the aid of power difference between the overlapped UEs, as illustrated in Figure 15.1. Therefore, the signal of the strongest UE (xU −1 ) is decoded first by treating the weaker UEs as noise and is removed from the superimposed signal (yi ) to detect the next strongest UE. The same procedure is followed until the signal of desired UE is obtained as follows: yi = hi Pi xi + zi , (15.2) i−1 √ where zi = hi u=0 Pu xu + wi represents IUI plus noise after SIC of ith UE in the case of correct detection of stronger UEs (u > i, and u ∈ {0, 1, . . . , U − 1}).

15.2.2 Uplink PD-NOMA PD-NOMA is applied for uplink transmission as illustrated in Figure 15.2. UEs are located in different distances from the BS that corresponds to different channel

464

Flexible and cognitive radio access technologies for 5G and beyond Power

Power

Freq. Time

Freq. Time Power

Detect strong UE

Freq.

Subtract strong UE

Detect weak UE

Time

Figure 15.2 A scenario of uplink NOMA with U = 2 and SIC is performed by the BS

gains. The superimposed signal reaching the BS from multiple UEs is expressed as follows: yi =

U −1 P i hi xi + Pu hu xu +wi ,

u=0, u=i desired signal

(15.3)

IUI

where hu corresponds to the channel of the uth interferer UE. At the receiver, different from downlink PD-NOMA, SIC process is executed by the BS considering the descending order of UEs’ channel gains [2,6]. Thus, the signal of strongest UE (xU −1 ) is decoded, while treating the IUI as noise. BS detects the signal of weak UEs (xi ) after removal of U − i − 1 stronger UEs ({xi+1 , . . . , xU −1 }) via SIC.

15.2.3 Capacity in PD-NOMA In PD-NOMA, the enhancement of achievable sum rate due to the sharing of radio resources between multiple UEs has attracted the interest of both academia and industry. In order to have beneficial insights about the capacity of PD-NOMA, theoretical analyses of system capacity are provided for downlink PD-NOMA and compared with OFDMA. According to (15.2), under the assumption of successful SIC without error propagation, the sum rate of the near UE is written as Ri = Blog2 1 + i−1 u=0,

Pi |hi |2 Pu |hi |2 + Bσw2i

,

(15.4)

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465

where B denotes the signal bandwidth in terms of Hz. Different from PD-NOMA, in OMA, the available spectrum is split among the UEs and thus the achievable rate by ith UE is written as Pi |hi |2 Ri = βi log2 1 + , (15.5) βi σw2i where 0 ≤ βi ≤ B Hz, and the total achievable rate equals R=

U −1

Ru .

(15.6)

u=0,

In Figure 15.3, a comparison of PD-NOMA and OFDMA is shown in terms of achievable sum rate. Three different channel condition pairs, i.e., |h1 |2 = 20 dB and |h2 |2 = 10 dB, |h1 |2 = 20 dB and |h2 |2 = 0 dB, and |h1 |2 = 10 dB and |h2 |2 = 0 dB, are considered for the near UE and the far UE, respectively. The overall transmission bandwidth is adjusted as B = 1 Hz for the sake of simplicity. In the case of OMA, the total transmitted power and transmission bandwidth are equally split between two UEs as (P1 = P2 = P/2, β1 = β2 = B/2). As shown in Figure 15.3, PD-NOMA provides higher achievable sum rate than the conventional OMA. The achievable rate of the far UE corresponds to R2 = 0.4 bits/s/Hz and R2 = 0.79 bits/s/Hz for OMA 3.5 NOMA (|h1|2 = 20 dB, |h2|2 = 10 dB) OMA (|h1|2 = 20 dB, |h2|2 = 10 dB)

3

Far UE (R2, bits/s/Hz)

NOMA (|h1|2 = 20 dB, |h2|2 = 0 dB) OMA (|h1|2 = 20 dB, |h2|2 = 0 dB)

2.5 R2 = 2.23 bits/s/Hz)

NOMA (|h1|2 = 10 dB, |h2|2 = 0 dB) OMA (|h1|2 = 10 dB, |h2|2 = 0 dB)

2 R2 = 1.38 bits/s/Hz)

1.5

R1 = 4 bits/s/Hz)

1 R2 = 0.79 bits/s/Hz)

0.5 R2 = 0.4 bits/s/Hz)

0 0

1

2

3

4

5

6

7

Near UE (R1, bits/s/Hz)

Figure 15.3 The achievable sum rate for OMA and downlink PD-NOMA

466

Flexible and cognitive radio access technologies for 5G and beyond 18 OMA, near UE OMA, total NOMA, near UE NOMA, total Far UE

16

Capacity (bits/s/Hz)

14 12 10 8 6 4 2 0

5

10

15

20

25

30

Eb/No (dB)

Figure 15.4 Comparison of total achievable sum rate between OFDMA and downlink PD-NOMA for R2 = 2 bits/s/Hz as a function of signal-to-noise ratio

and PD-NOMA schemes, respectively, while R1 = 4 bits/s/Hz with (|h1 |2 = 20 dB, |h2 |2 = 0 dB). Moreover, PD-NOMA provides R2 = 2.23 bits/s/Hz for the far UE under the condition of (|h1 |2 = 20 dB and |h2 |2 = 10 dB), where OMA offers R2 = 1.38 bits/s/Hz. In PD-NOMA, the achievable sum rates for two UEs are increased due to the power allocation considering the channel gain difference. Moreover, in Figure 15.4, the capacity of the near UE and total rate are shown for OMA and PDNOMA schemes considering the desired capacity for the far UE R2 = 2 bits/s/Hz under the condition of P1 = P/5, P2 = 4P/5.

15.2.4 Fairness in PD-NOMA OMA offers high throughput for the near UEs due to the high signal-to-noise ratio and interference-free transmission manner. On the other hand, the throughput of the far UEs significantly reduces since the channel gain decays with the distance between the UE and BS due to path loss. Therefore, UEs are associated with unequal achievable rates that destroy the system fairness in the conventional OMA schemes. In this sense, PD-NOMA performs power allocation considering the demands of UEs and their channel gains. PD-NOMA offers a more flexible utilization of radio resources and power constraint and consequently improves the fairness of UEs’ achievable rate in a more efficient way, compared with OMA. For this reason, max–sum rate and max–min rate fairness criteria are widely adopted in the literature. Max–sum rate

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corresponds to the maximization of the total achievable rate that is expressed as follows: max Pi

subject to

U −1

Ri ,

(15.7a)

Pi ≤ P,

(15.7b)

0 ≤ Pi , ∀i,

(15.7c)

ˆ F = F,

(15.7d)

i=0 U −1 i=0

where F and Fˆ denote the targeted fairness and the calculated fairness for a given power allocation factors, respectively. On the other hand, max–min rate is the maximization of minimum UE rate. At the receiver, exhaustive search is used to find the optimum Pi factors that meet the fairness constraint [7]. A quantitative measure of fairness F is given in [8] as follows: −1 2 ( Ui=0, Ri ) (15.8) F = U −1 2 , U i=0, Ri where system fairness corresponds to F = 1 when all the UEs have the same rate, given that 0 ≤ F ≤ 1. According to Figure 15.3 and (15.8), OMA and PD-NOMA schemes offer fairness of (FO = 0.59 and FN = 0.69) and (FO = 0.80 and FN = 0.92) under the conditions of (|h1 |2 = 20 dB and |h2 |2 = 0 dB) and (|h1 |2 = 20 dB and |h2 |2 = 10 dB), respectively. In other words, PD-NOMA enables one to increase the achievable capacity of the far UE with the aid of proper power allocation considering the individual channel gains and difference between them. Hence, PD-NOMA provides higher system capacity and fairness compared with OMA.

15.3 State-of-the-art NOMA solutions In this section, three fundamental multicarrier NOMA techniques, including LDSOFDMA that is also named multicarrier LDS multiple access, pattern division multiple access (PDMA), and IM multiple access, are discussed for beyond 5G wireless networks [9–11].

15.3.1 Low-density spreading orthogonal frequency division multiple access LDS-OFDM combines low-density spreading with OFDM in order to control IUI and enhance the system capacity with a feasible MUD at the receiver. In LDS-OFDM, modulated data symbols of the desired UE are spread over a fixed set of N subcarriers that are determined by the LDS signature matrix in an OFDM block, as illustrated in Figure 15.5.

468

Flexible and cognitive radio access technologies for 5G and beyond UE 1

...

UE 1

S1,1, S1,2, ..., S1,V

UE M

S2,1, S2,2, ..., S2,V

SU,1, SU,2, ..., SU,V

N radio resources

...

Figure 15.5 A transmission with LDS-OFDM UE 1

UE 2

UE 4

UE 3

UE 5

UE 6 Three UEs sharing the first radio resource

=

+

+

+

+

+ Four UEs sharing the last radio resource

Figure 15.6 A PDMA block with six UEs on three radio resources

The number of spreading chips is equal to the number of subcarriers. The spread data is generated as xi = Si ξi , where Si is spreading matrix, and ξi = [si,1 , si,2 , . . . , si,V ], where si,γ ∈ S, where S is the set of M -ary symbols S = {s0 s1 · · · sM −1 }. V is the number of data symbols carried by ith UE. LDS-OFDM provides a new way to manage IUI compared with the conventional PD-NOMA since UEs do not cause interference over all shared radio resources. To exemplify, in Figure 15.5, U UEs share a total of N subcarriers, and only two UEs collide over a given subcarrier. For a given scenario, the overloading factor corresponds to UV /N . At the receiver, MUD is performed via message-passing algorithm (MPA) to mitigate the IUI due to collision. MPA scheme can achieve a near-optimal performance in MUD. Different from LDS-OFDM, in sparse code multiple access (SCMA) [12], not only the LDS design but also the symbol constellation are jointly utilized to enhance the system gain. Hence, data bits of UE are directly mapped to a fixed multidimensional constellation that is named codeword. Integration of joint MUD in SCMA enhances the reliability of the communication system at the cost of high receiver complexity.

15.3.2 Pattern division multiple access PDMA enables the multiplexing of UEs in time, frequency, space, and their different combinations. Specifically, PDMA is similar to SCMA when code-domain multiplexing is performed. However, the number of subcarriers used to spread a given data symbol can be different among UEs [13]. Figure 15.6 illustrates a simple example of radio resource allocation via PDMA pattern.

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The first UE uses all the resources while the last UE utilizes only one resource. Therefore, corresponding pattern matrix for PDMA considering all UEs is expressed as follows: ⎡

GPDMA

⎤ 101100 = ⎣1 1 0 0 0 1⎦, 111010

(15.9)

where xi is not mapped to the corresponding radio resource if the element of GPDMA equals “0.” Moreover, the number of UEs superimposed in a given radio resource can be controlled by the design of GPDMA . Considering (15.9), three UEs and four UEs share the first and the last radio resources, respectively. In other words, IUI in the first resource is less than the remaining ones. At the receiver side, not only MPA, but also SIC can be utilized to perform MUD. The UE, who uses all the radio resources, needs to be detected first by treating the other UEs as noise since it has the highest diversity. Hence, receiver complexity is significantly simplified.

15.3.3 Index modulation in NOMA IM is a new concept that has attracted the attention of both academia and industry due to the sparse structure of the generated signal with IM. It has experienced a proliferation after its combination with the classical OFDM signal and called OFDMIM [14]. Briefly, OFDM-IM can outperform OFDM in terms of spectral efficiency (SE), energy efficiency, and error performance. Moreover, OFDM-IM is more robust against interference compared with OFDM due to fractional subcarrier activation [15,16]. Readers are referred to revisit Chapter 6 for more insights. Motivated from the inherent immunity of OFDM-IM against interference, in [17], resource sharing between multiple UEs with OFDM-IM is proposed for uplink transmission and named IM-OFDMA. OFDM block is split into a fixed number of subblock that is shared between a subset of UEs. Each UE transmits data informations not only via modulated subcarriers but also via the indices of the active subcarriers. Since all the subcarriers are not utilized, collision between UEs is controlled. At the receiver, maximum likelihood receiver is used to find both active subcarriers and conventional data symbols. The system reliability is controlled by the sparsity of the subblocks. Low subcarrier activation ratio offers a high sparse block design that can further reduce IUI and enhance the UE separability. Use of different subcarrier activation ratios considering the UE demands can also allow one to control IUI while satisfying the requirements of UEs. It is important to note that PD multiplexing is not considered in [17]. The performance of IM-OFDMA can be further increased by joint utilization of power and index dimensions. IM-OFDMA suffers from a complex receiver design that needs to be further investigated. Actually, principles of LDSOFDM, PDMA, and IM-OFDMA can be considered the same. The goal is to allow intelligent control of IUI while satisfying the demands via sparse resource utilization.

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15.4 Grant-free random access techniques Massive machine type communications (mMTC), ultra-reliable low latency communication (URLLC), and its advanced versions, such as secure URLLC (SURLLC), put pressure on service providers due to the requirements of massive connectivity, ultrareliability, and low latency. In order to satisfy these requirements simultaneously, GF random access is considered as the most prominent technology for uplink transmission by academia and industry. It allows avoiding latency caused by handshaking between UE and BS for scheduling and granting. GF access can be performed through dedicated resources and a resource pool (shared resources), where orthogonal and non-orthogonal transmission is performed, respectively. The resource pool consists of multiple resource blocks where each UE randomly selects. Therefore, transmission over the shared resources suffers from low reliability due to collision. On the other hand, assigning the available resource to a single UE leads to a decrease in SE. For the purpose of controlling IUI, various resource utilization schemes are investigated in the literature.

15.4.1 Transmission schemes In this section, the existing GF transmissions are revised and then their advantages and disadvantages are discussed from the perspective of interference management.

15.4.1.1 Reactive transmission Reactive GF transmission is performed as shown in Figure 15.7, where Ttx denotes a single-transmission time interval (TTI), while Tpr refers to processing time for the transmitted data. Tpr is assumed the same for both UE and BS, and equals 1 TTI that is dependent on subcarrier spacing (SCS) for OFDM systems. In reactive scheme, UE waits a feedback (i.e., acknowledgment (ACK) or negative acknowledgment (NACK)) from BS after its each transmission [18]. Therefore, 4 TTIs (i.e., 1 TTI→ packet transmission, 1 TTI→ processing time at BS, and 1 TTI→ feedback transmission, 1 TTI→ processing time at UE) are required for every transmission that needs to be done. This leads to inevitable latency to achieve desired reliability for URLLC. Reactive GF access has no unique interference compensation scheme and consequently leads to low reliability for latency-critical applications.

UE T pr

T pr T tx

T tx

T pr T tx

T tx

BS TTI

T pr

ACK/NACK

T pr

Figure 15.7 Reactive GF access

ACK/NACK

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15.4.1.2 K-Repetitions GF access through K-Repetitions is illustrated in Figure 15.8. Besides initial transmission, a predefined number (K) of retransmissions are performed consecutively [18]. Different from the Reactive GF access, the feedback is sent by BS after K retransmissions in order to allow multiple transmissions within a given latency constraint. Hence, (K+3) TTIs (i.e., K TTI→ packet retransmission, 1 TTI→ processing time at BS, and 1 TTI→ feedback transmission, 1 TTI→ processing time at UE) are required for each K retransmissions. Total transmission latency corresponds to (K+5) TTI due to the alignment time and the first transmission. Later, BS can combine these repetitions to improve the transmission reliability. Multiple transmissions over a resource pool provide an opportunity to compensate the effect of collision stemming from the other UEs. However, unnecessarily low or high values of K lead to low reliability or decrease in SE, respectively. Therefore, selection of the optimum K value is the primary issue for GF access with K-Repetitions. It is important to note that GF access with K-Repetitions corresponds to Reactive transmission in the case of K = 0.

15.4.1.3 Proactive transmission In order to avoid a decrease in either SE or reliability, stemming from K value, a UE with Proactive GF transmission monitors the feedback while performing consecutive retransmissions [18]. As shown in Figure 15.9, the UE receives the feedback after the first retransmission is completed. In this figure, NRep shows the number of repetitions, where positive feedback reaches the UE. Since NRep is not predefined, it results in low latency in case the desired reliability is satisfied and vice versa. Thus, (NRep +2) TTI (i.e., NRep TTI→ packet retransmission, 1 TTI→ processing time at BS, and 1 TTI→ feedback transmission, 1 TTI→ processing time at UE) are required, and total latency corresponds to (NRep +4) TTI due to alignment and the first transmission. As in GF access with K-Repetitions, multiple retransmission of a packet in a short time mitigates the effect of collision in Proactive transmission. However, it results in complexity at the UE side due to simultaneous packet transmission and feedback reception.

r=0

r=1

r=K

...

UE

T pr

T pr T tx

T tx

T tx

T tx

T tx

...

BS TTI

K-Repetitions

T pr

ACK/NACK

Figure 15.8 K-Repetitions GF access

472

Flexible and cognitive radio access technologies for 5G and beyond r=0

r=1

r=2

UE

...

r = N Rep

T pr

T pr T tx

T tx

T tx

...

T tx

T tx

BS TTI

ACK/NACK

Figure 15.9 Proactive GF access

15.4.2 Adaptive resource utilization In all the earlier mentioned transmission schemes, only the repetition mechanism can be considered as an interference avoidance technique. However, intelligent resource utilization techniques are required along with the well-known technique, i.e., retransmission, in order to allow collision-prone transmission in 5G beyond networks. In [19], retransmissions over the dedicated resources are proposed when the first transmission is unsuccessful due to GF access over the shared resources. In this way, there is no need to use dedicated resources if the transmission is successful. In a reverse manner, in [20], the same authors propose to utilize the dedicated resources and the resource pool for the first transmission and retransmissions of UEs, respectively. As shown in Figure 15.10, once the first transmission is performed, U UEs transmit their packet NRep times in the resource pool with K resource block, given that K < U , without waiting for the feedback from the BS. In the meanwhile, BS attempts to decode the initial transmissions of the UEs and store the successfully decoded signals. When UEs with the successful initial transmissions collide with a UE with the unsuccessful initial transmission, BS can decode its data by the prior information of the other UEs’ packets. However, it is not guaranteed that the undecoded UE overlaps with the decoded ones in the shared resources. It is important to note that this scheme can be considered as a novel NOMA scheme utilizing additional prior knowledge. In [21], joint resource configuration and modulation and coding scheme selection is proposed in order to control IUI in the shared resources. The UE with weak channel condition use low-order modulation, while the UE with strong channel condition performs its transmission with high-order modulation. Thus, the weak UE and the strong UE experience partial and fully overlapping, respectively, when the collision between them occurs. The occupied bandwidth linearly reduces with the increase of modulation order. Consequently, in order to enable successful GF transmission over the shared resources, UEs should collide in such a way that they can be separable at the receiver with the aid of prior information about the UEs. To exemplify, as explained in Section 15.3, SCMA and low-density spreading OFDMA (LDS-OFDMA) allow a collision in time and frequency by the means of separability in code domain, where each UE has an unique code.

Non-orthogonal radio access technologies 1

2

3

UE 1

UE 2

UE 3

1

Initial transmission over N dedicated radio resources

... ...

2

...

...

...

473

UE N

Retransmission over K shared radio resources

...

|h2 |2 . This is because user 1 has a larger channel gain, and assigning more resource to the user with larger channel gain yields a higher sum rate [1]. Now let us generalize the simple two-user NOMA system into a multiuser one. Denote the number of users by K, and it is assumed that the users are indexed in a descending order of their channel gains, i.e., |h1 |2 ≥ · · · ≥ |hK |2 . To ensure system fairness and facilitate the signal decoding, a larger portion of power is allocated to users with smaller channel gains, i.e., α1 ≤ · · · ≤ αK , with αk as the power coefficient for user k, k = {1, . . . , K}, satisfying Kk=1 αk = 1. At the receiver side, SIC is performed at user k, k = {1, . . . , K − 1} to remove the interference from the users

7 6.5 6

Sum rate

5.5 NOMA: |h1|2/h2|2 = 10

5

OMA: |h1|2/h2|2 = 10 NOMA: |h1|2/h2|2 = 5

4.5

OMA: |h1|2/h2|2 = 5

|h1|2/h2|2

4

NOMA: |h1|2/h2|2 = 1 OMA: |h1|2/h2|2 = 1

3.5 3 0

0.2

0.4

0.6

0.8

1

Power coefficient 1

Figure 19.3 Sum rate comparison between NOMA and OMA under different channel gain disparities

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with smaller channel gains, i.e., user set k + 1 to K. Accordingly, the achievable rate of user k is given by αk P|hk |2 ραk |hk |2 NOMA Rk = log2 1 + k−1 = log2 1 + k−1 , 2 2 ρ i=1 αi |hk |2 + 1 i=1 αi P|hk | + σ k−1

k−1

(19.6)

where i=1 αi is assumed to be zero for k = 1. When k = 1, the part ρ i=1 αi |hk |2 denotes the remaining inter-user interference after SIC, which comes from the users with better channel conditions. In theory, the number of users served using downlink NOMA is unlimited. However, in practice, downlink NOMA is usually applied to a small number of users, typically with K = 2 or 3. This is because when K becomes large, the performance degradation in a bit error rate becomes severe due to error propagation from imperfect SIC. Meanwhile, more computing power and higher energy consumption are required to decode other users’ signal, which makes it less attractive for resource-constrained user devices. When the number of users in the system is large, user scheduling is required. Users are first divided into different groups. Then, users in the same group are served using NOMA, while different groups are served using OMA, such as time division multiple access or frequency division multiple access.

19.2.2 Uplink NOMA Figure 19.4 shows an uplink NOMA system with two users. Denote the transmitted signals from users 1 and 2 by s1 and s2 , respectively. The signal received at the BS can be expressed as y=

2

Pk hk sk + n,

(19.7)

k=1

where Pk denotes the transmitted power of user k. The user signal sk is of unit power, i.e., E{|sk |2 } = 1. n represents the AWGN noise, with zero-mean and variance σ 2 .

SIC User 1’s signal decoding

Subtract user 1’s signal

User 2’s signal decoding

User 2

User 1

Figure 19.4 Uplink NOMA system with two users

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Flexible and cognitive radio access technologies for 5G and beyond

In uplink NOMA, the user signals are all decoded at the BS, which owns the global information. Therefore, the decoding order can be flexible. Moreover, each user has its own power constraint, and there is no constraint on the comparative values of their transmitted powers. Nonetheless, from the practical point of decoding, the BS prefers to first decode the strongest received user signal. The received signal strength is the multiplication of the transmitted signal strength and the channel gain. In particular, when two users transmit with the same power, then the user with larger channel gains is decoded first. This is exactly the opposite of downlink, where the weak user’s signal is decoded first. Without loss of generality, we assume that the signal strength of user 1 is larger than that of user 2 at the BS. Then, the achievable rates at the users are given by RNOMA = log 2 1+ 1 RNOMA 2

P1 |h1 |2 P2 |h2 |2 + σ 2 P2 |h2 |2 . = log2 1 + σ2

,

(19.8a) (19.8b)

After some algebraic manipulations, the sum rate can be expressed as P1 |h1 |2 + P2 |h2 |2 1 + . RNOMA = log 2 s σ2

(19.9)

It can be easily verified that the same sum rate as in (19.9) is achieved when user 2 is decoded earlier. This indicates that the sum rate of uplink NOMA does not depend on the decoding order. For the comparison between NOMA and OMA, it can be shown that the same sum rate is achieved if OMA adopts the optimal split of degrees-of-freedom [39]. In this case, using NOMA does not provide the performance gain. However, it does provide an alternative to satisfy the individual rate requirement of the users, which may not be met by OMA in certain cases [40]. In addition, the optimal splits of degrees-of-freedom are often not even for the two users. However, in practice equal split of degrees-of-freedom is preferred. In this case, NOMA still outperforms OMA. Now let us consider the general NOMA system with K users. It is assumed that the received user signals are indexed in a descending order, i.e., P1 |h1 |2 ≥ · · · ≥ PK |hK |2 . Accordingly, the achievable rate of user k is given by RNOMA = log2 1 + K k

Pk |hk |2

i=k+1

Pi |hi |2 + σ 2

,

(19.10)

where Ki=k+1 Pi |hi |2 is assumed to be zero for i = K. Uplink NOMA can support more users than downlink NOMA, since the BS is usually equipped with more powerful computing units. As a result, uplink NOMA is more appropriate for massive machine type communications (mMTC) [41].

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19.3 Fundamentals of PLS Wireless communication networks are particularly vulnerable to eavesdropping and impersonation attacks due to the broadcasting nature of wireless channels. A basic security requirement is to make sure that eavesdroppers cannot read confidential messages. Traditionally, this is achieved via employing cryptographic algorithms. PLS represents an alternative approach, which is implemented on physical layer by exploring the randomness nature of wireless channels. The origin of PLS research can be traced back to Shannon’s information theoretic secrecy analysis [42], which defined that security level depends on the amount of information known by the eavesdroppers. A perfect secrecy can be achieved when the eavesdroppers ignore the transmitted information completely, except for just randomly guessing the original information bit by bit. This description on security is related closely to the communications in the presence of noise, so that the concepts of entropy and equivocation developed for communication problems provide a direct inspiration in the early investigations on PLS [22,43]. The confidential communications can achieve a maximum message transmission rate using wiretap channel coding, whose rate is defined as the secrecy capacity by Wyner [22]. Actually, Wyner only showed that it is possible to implement secure communications in degraded broadcast channels. PLS concepts have become more popular with the introduction of non-degraded channels [44], Gaussian channels [45,46], small-scale fading channels [47–50], multiantenna channels [51–54], and relay channels [55,56].

19.3.1 Information-theoretic secrecy In [22], Wyner has shown a basic wiretap channel consisting of a transmitter, a legitimate receiver, and an eavesdropper. The confidential information, M , is encoded into a message X n and then transmitted from the transmitter to the legitimate receiver, while the eavesdropper tries to overhear this information. Note that here n denotes the codeword length. At the legitimate receiver and the eavesdropper, the message is denoted as Y n and Z n , respectively. From the Shannon’s definition, perfect secrecy is obtained when the original message and the eavesdropper’s observation are statistically independent. It is formulated as H (M ) = H (M |Z n ) or I (M ; Z n ),

(19.11)

where H (M ) is the entropy of the information source, H (M |Z ) is the entropy of the observation at the eavesdropper, and I (M ; Z n ) denotes the mutual information between M and Z n . In the literature, strong secrecy and weak secrecy are often used in secrecy analysis. The strong secrecy requires the original confidential information and the observation of the eavesdropper be asymptotically statistically independent when the codeword length approaches infinity. The strong secrecy is mathematically presented as n

lim I (M ; Z n ) = 0.

n→∞

(19.12)

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Flexible and cognitive radio access technologies for 5G and beyond

Meanwhile, the weak secrecy requires the rate of information leaked to the eavesdropper disappear as the codeword length approaches infinity. The weak secrecy is mathematically formulated as lim

n→∞

1 I (M ; Z n ) = 0. n

(19.13)

19.3.2 Metrics 19.3.2.1 Ergodic secrecy capacity For a system in which the encoded message spans over sufficient channel realizations to experience the ergodic features of the fading channel, the ergodic secrecy capacity captures the capacity limit subject to the constraint of information-theoretic secrecy. For one fading realization, the wireless channel can be regarded as a complex AWGN channel. The secrecy performance of an AWGN wiretap channel can be measured by the secrecy capacity [46], which can be regarded as the maximum achievable transmission rate subject to not only the reliability constraint but also the requirement of information-theoretic secrecy [22]. The secrecy capacity for a single-wiretap fading channel realization is given as Cs = [CM − CE ]+ ,

(19.14)

where [x]+ = max (x, 0). The instantaneous legitimate and eavesdropping capacities are, respectively, given as CM = log2 (1 + γM ),

(19.15)

CE = log2 (1 + γE ),

(19.16)

with γM = P|hM |2 /δM and γE = P|hE |2 /δE as the instantaneous received SNR. Here P is the transmit power, hM is the legitimate channel, hE is the eavesdropping channel, and δM and δE are the receiver noise variances. To obtain the ergodic secrecy capacity, two scenarios of the available channel state information (CSI) at the transmitter are taken into consideration, i.e., full CSI and legitimate CSI. In the first scenario, the transmitter knows the CSI of both the legitimate and eavesdropping channels, and thus, the transmitter only transmits information when the SNR of the legitimate channel is better than that of the eavesdropping channel, i.e., γM > γE . The ergodic secrecy capacity is calculated by taking the average of the secrecy capacity over all fading realizations and is given as C¯ sF =

∞ ∞ ( log2 (1 + γM ) − log2 (1 + γE ))fγM (γM )fγE (γE )dγM dγE .

(19.17)

0 γE

In the case of only legitimate CSI, available at the transmitter, the ergodic secrecy capacity is given as C¯ sL =

∞ ∞ 0

0

[ log2 (1 + γM ) − log2 (1 + γE )]+ fγM (γM )fγE (γE )dγM dγE .

(19.18)

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19.3.2.2 Secrecy outage probability In [57], the definition of secrecy outage probability (SOP) is detailed as follows: P(Cs < Rs ),

(19.19)

where Cs is the secrecy capacity and Rs > 0 is the target secrecy rate. This defines SOP as the probability that the instantaneous secrecy rate of the considered user falls below a predefined target data rate. To differentiate the measure of secrecy level, an alternative definition of SOP is given as [58] P(CE > RM − Rs | message transmission),

(19.20)

where RM is the rate of transmitted codeword and Rs is the rate of the confidential information. This measures the probability that a transmitted message does not satisfy the secrecy requirement.

19.4 PLS-enhanced NOMA The community has shown great interest in enhancing the PLS of NOMA systems. In the following, we categorize the existing works into three classes according to the number of antennas at the BS, namely, SISO–, MIMO–, and massive MIMO–NOMA systems.

19.4.1 PLS in SISO–NOMA systems Here, all nodes are assumed to be equipped with a single antenna. A typical system model is given in Figure 19.5, which consists of a BS, K legitimate users, and an eavesdropper. The BS sends the superposed signals √ to the legitimate users using NOMA, and the transmitted signal is given by Ki=1 Pi si , where si and Pi denote the

…... User K

Eve Base station

User 1

Figure 19.5 A SISO–NOMA system with K legitimate users and an eavesdropper

600

Flexible and cognitive radio access technologies for 5G and beyond

normalized message and the transmit power for user i, respectively. Then, the signals received at user k and the eavesdropper are expressed as yk = hk

K

Pi si + n k ,

(19.21)

P i si + n e ,

(19.22)

i=1

and ye = he

K

i=1

where hk and he denote the channel gains between the BS and user k and eavesdropper, respectively. Meanwhile, nk and ne represent the zero-mean AWGN at user k with variance σk2 and that at eavesdropper with variance σe2 , respectively. For simplicity, it is assumed that the noise variances at all users and the eavesdropper are equal, i.e., σ12 = · · · = σK2 = σe2 = σ 2 . Assume that the users are arranged in a descending order of their channel gains, i.e., |h1 |2 ≥ · · · ≥ |hK |2 . According to the NOMA principle, the achievable rate at user k is given by Pk |hk |2 b Rk = log2 1 + k−1 . (19.23) 2 2 i=1 Pi |hk | + σ The eavesdropper attempts to intercept the message intended to the legitimate users. Most works assume that the eavesdropper owns the ability of SIC as the legitimate users [24,25,27,59–61]. This implies that the eavesdropper has already decoded the messages for all users ∀i > k, before the decoding of the message for user k. Accordingly, the achievable rate at the eavesdropper is given by [25,28,61] Pk |he |2 e Rk = log2 1 + k−1 . (19.24) 2 2 i=1 Pi |he | + σ Then, the secrecy rate achievable by user k is given by [22]

+ Rsk = Rkb − Rke .

(19.25)

It is clear that Rkb ≤ Rke if |hk |2 ≤ |he |2 , resulting in Rsk = 0. Based on the rate expression in (19.25), the maximization of the sum secrecy rate was investigated in [61] for a multiuser scenario, subject to users’ quality-ofservice (QoS) and total transmit power constraints. The feasible region of the transmit power to satisfy the users’ QoS constraints was first identified. Then, in the case of a feasible scenario, the optimal power allocation solution was derived in closed-form expression. The derived results show that the sum secrecy rate is maximized when the extra power is only used for increasing the rate of the strongest user. This, however, yields system unfairness. To address this, the max–min secrecy rate problem was

5 A few works further assume that the eavesdropper does not experience interference from other users. This clearly overestimates the eavesdropper’s capability and is adopted mainly for analytical simplicity [28].

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formulated in [60] subject to the secrecy outage and transmit power constraints. The optimal power allocation solution was obtained using the bisection search. Except for the rate maximization, the transmit power minimization was also considered [60]. A closed-form power allocation solution was derived to minimize the transmit power under the secrecy outage and QoS constraints. In addition to the secrecy rates, the SOP has also been considered [28,59]. In [28], the authors studied the secrecy performance of a large-scale NOMA network, where multiple eavesdroppers wiretap the signal transmission of a randomly selected NOMA pair. A protected zone was invoked to improve the PLS of the considered system. Exact and asymptotic SOP expressions were derived for the selected NOMA pair, showing that the secrecy diversity order is determined by the weak user. To enhance the secrecy performance, user selection was also considered in [59] for a NOMA system consisting of one BS, multi-near users, multi-far users, and one eavesdropper. The authors proposed to select the strongest user from both user groups, and further derived the exact and approximated SOP expressions for the selected near and far users, respectively. Moreover, a descent-based search method was proposed to obtain the optimal power allocation that minimizes the overall SOP. The combination of NOMA with other advanced transmission technologies, e.g., SWIPT, relay, and FD, has attracted great attention [24–27]. In the following, we discuss the PLS in such systems. SWIPT: In [24], a NOMA system with SWIPT was considered, which consists of a BS, multiple users, and an eavesdropper. In particular, the BS adopts NOMA to transfer power and information to the users simultaneously, while the users only employ the harvested energy to decode the information by employing the power splitting method. The sum secrecy rate maximization problem was formulated under the individual user’s minimum rate and harvested energy constraints. Both power allocation and power splitting ratio selections should be considered for the formulated problem. To address it, the closed-form expressions for the optimal power splitting ratio were first derived. On this basis, power allocation was converted to a convex optimization problem and solved accordingly. Cooperative NOMA: In [25], a cooperative NOMA system was considered, including a BS, two users, a relay, and an eavesdropper. It was assumed that no direct link exists between BS and the users, as well as the eavesdropper. Both amplify-andforward (AF) and decode-and-forward (DF) protocols were considered for relaying. The derived SOP results showed that AF and DF achieve nearly the same secrecy performance. In [26], the authors considered a NOMA two-way relay system consisting of multiple preassigned user pairs. The secrecy EE maximization problem was formulated, requiring a joint design of the subcarrier assignment and power allocation. The subcarrier assignment was handled using many-to-many matching, and on this basis, the power allocation was solved using geometric programming. FD: In [27], a cooperative NOMA system was considered, where two sourcedestination pairs share a common FD DF relay. The direct links from the sources to destinations and eavesdroppers were assumed to be unavailable. As a result, uplink NOMA was used by the sources to send information to the relay, while downlink NOMA was used by the relay to forward information to the destinations. During information forwarding, the relay added AN to defend against eavesdropping. The

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optimal power allocation between the information and the AN signal was obtained to maximize the system capacity. Moreover, analytical and numerical results showed that the proposed scheme significantly outperforms the joint NOMA and AN in halfduplex relay scheme in terms of SOP.

19.4.2 PLS in MIMO–NOMA systems As shown in Figure 19.6, multi-antenna technology can be applied to NOMA to form MIMO–NOMA, which can further improve the system performance [14–16]. In MIMO–NOMA systems, two different schemes exist: one is to select only one antenna for transmission, referred to as the transmit antenna selection (TAS) scheme, while the other one is to use all antennas for transmission, referred to as beamforming scheme. In the following, we present these two schemes in detail. TAS: The authors in [62] proposed a novel TAS scheme to enhance the PLS for a two-user downlink NOMA system. By selecting the antenna that minimizes the maximum capacity of the eavesdropper channels, the proposed scheme aimed to select an antenna that is most robust against the interception of an eavesdropper. Note that perfect CSI of the wiretap link is required by the TAS scheme in [62]. The authors in [63], on the other hand, considered both cases with/without the CSI of the wiretap link. When the CSI of the wiretap link is available, the antenna that maximizes the secrecy capacity was selected. With two users in the system, the secrecy rate of each user can be adopted as secrecy capacity. When the CSI of the wiretap link is unavailable, a suboptimal antenna selection scheme was adopted, which selects the antenna to maximize the transmission capacity. The proposed TAS schemes were compared with the traditional space–time transmission scheme wherein all antennas were utilized

Nt antennas

UE

UE Cluster 1

UE Cluster M

UE UE

UE

Eavesdropper

Figure 19.6 A typical MIMO–NOMA system with an eavesdropper

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to transmit with equal power, and its superiority was verified. Note that only one eavesdropper was considered in [62,63]. In contrast, the authors in [64] considered the general scenario with multiple eavesdroppers. Moreover, both non-colluding and colluding eavesdroppers were considered, respectively. In the case without collusion among the eavesdroppers, only the strongest eavesdropper was active. Otherwise, all eavesdroppers were active and their received signals were combined. For both cases, a max–min TAS scheme was proposed, which aimed to select the antenna that maximizes the minimum secrecy rates among the two users. This way, the performance of both users was considered simultaneously, and system fairness was improved when compared with the schemes in [63], where only one user was considered. Beamforming: The transmit beamforming matrix can be obtained either following existing beamforming strategies or by solving specific optimization problems. One popular beamforming strategy is the maximum ratio transmission (MRT), which has been adopted for different MIMO–NOMA systems considering PLS [28–30]. Specifically, the authors in [28] studied the PLS of a large-scale MIMO–NOMA network by applying stochastic geometry. MRT was used for beamforming each user’s signal, and on this basis, exact expressions of the SOP were derived. Moreover, the asymptotic SOP was derived under an infinite number of transmit antennas. The authors in [29] applied NOMA to a multiuser system with mixed unicasting and multicasting traffic. The multicasting message was to be received by all users, while the unicasting message was intended only for a particular user. MRT based on the effective channel gain of the unicasting user was applied to all users. As a result, the difference between the users’ effective channel gains was created artificially, making it suitable for NOMA transmission. It was shown that NOMA-assisted multicast–unicast scheme brings a significant improvement in SE and secrecy unicasting rate over OMA-based counterparts. The authors in [30] considered an orthogonal frequency division multiplexing (OFDM) based MIMO–NOMA system empowered by wireless power transfer. In such a system, the transmission process consisted of two phases: the downlink wireless power transfer phase and the uplink information transmission phase. MRT was adopted for both phases. On this basis, an iterative algorithm was proposed, which jointly optimizes time, power, and subchannel allocation to obtain secure and energy-efficient transmission among multiple users. Presented results verified the superiority of the proposed scheme over conventional orthogonal frequency division multiple access systems and other existing NOMA-based schemes. The beamforming matrix was obtained by solving the corresponding optimization problems in [65–67]. More exactly, the authors in [65] considered a MIMO–NOMA cognitive radio network relying on SWIPT. The transmission power minimization problems were formulated under both perfect CSI and bounded CSI error models. Two approaches, one based on semidefinite relaxation and the other one based on a cost function, were proposed to solve these non-convex problems. The authors in [66] studied the sum secrecy rate maximization problem for a downlink MIMO–NOMA system with multiple legitimate users and an eavesdropper. Except for the transmit power constraint, the successful SIC constraints were considered in the problem formulation. The formulated problem was non-convex and first transformed into a biconvex problem based on the relationship between mutual information rate and

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minimum mean square error. Then, the biconvex problem was handled using alternating optimization method, where a second-order cone programming was solved in each iteration. The authors in [67] investigated a downlink MIMO–NOMA network with a BS, a central user, and a cell-edge user. In particular, the central user is an entrusted user while the cell-edge user is a potential eavesdropper. A secure beamforming optimization problem was formulated, which aims to maximize the secrecy rate of the central user under transmit power constraint at the BS and transmission rate requirement at the cell-edge user. The formulated non-convex problem was handled using majorization–minimization method, which iteratively optimizes a sequence of valid surrogate functions for the formulated problem. Moreover, in each iteration, a semi-closed form solution was derived to optimize the valid surrogate functions. Except for TAS and beamforming, multi-antenna can also be used for generating AN [31]. AN is often generated to lie in the null space of the intended legitimate user. This way, it causes confusion to the eavesdropper but does not degrade the information reception at the intended legitimate user [31]. AN-aided MIMO–NOMA systems were considered in [28,30,65]. More exactly, for a large-scale MIMO–NOMA system [28], AN was generated at the BS to further improve the security of the system. For a MIMO–NOMA cognitive radio network with SWIPT, an AN-aided cooperative jamming scheme was proposed in [65] to secure the primary network. In addition, AN was also considered for an OFDM-based MIMO–NOMA system empowered by wireless power transfer in [30]. As in SISO–NOMA, advanced transmission technologies, such as SWIPT, wireless power transfer, cooperative transmission, and mmWave drone, have been applied to MIMO–NOMA systems. Since the works considering SWIPT [65] and wireless power transfer [30] have been mentioned in the content earlier, we only introduce the works on cooperative transmission and mmWave drone in the following. Cooperative NOMA: The authors in [68] considered a DF-based cooperative NOMA system, where the information transmission from the relay can be wiretapped by the eavesdropper. To secure the information transmission, one relay’s antenna that can minimize the SOP of the opportunistic user was selected. In contrast, the authors in [69] studied an AF-based cooperative NOMA system, which consists of a multi-antenna source, a single-antenna relay, and destination. The relay was untrusted, that is, it also acted as an eavesdropper. When global CSI is available, the antenna that maximizes the secrecy rate was selected at the BS. To reduce the signaling overhead, a simple TAS scheme based only on the CSI of the source–destination link was also proposed. That is, the antenna that maximizes the link quality between the source–destination link was selected. mmWave drone: The authors in [36] studied an unmanned aerial vehicle (UAV)based network, where a UAV acts as an aerial BS to provide coverage. NOMA together with highly directional multi-antenna transmission technologies in mmWave frequency bands was utilized to improve the SE. To protect the legitimate users against potential eavesdropper attacks, a protected zone was introduced around the user region. However, the protected zone may not cover the entire eavesdropper region, due to resource limitation. To handle this, an approach to optimize the protected zone shape was proposed.

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19.4.3 PLS in massive MIMO–NOMA systems Massive MIMO is a key technology in 5G that offers better SE and EE [70]. The combination of massive MIMO and NOMA seems to come out naturally, since it can enhance the SE and increase the number of served users [71,72]. The system model in Figure 19.6 can also be used for describing a massive MIMO–NOMA system, but now the number of antennas Nt becomes quite large. The research on PLS in massive MIMO–NOMA systems is still in its infancy, and the works [32–35] are all we can find so far. In [32], the authors considered the case with an active eavesdropper that pretends to be a legitimate user. In this scenario, the eavesdropper used the same uplink training sequences as the legitimate users to obtain more information in the information transmission phase. The BS then used a specific channel estimation method to obtain the effective channels of NOMA clusters. Based on this effective CSI, the BS performed MRT to the clusters. The inter-user interference was exploited via a power allocation algorithm in which the uplink training transmit power of users and the transmit power at BS were allocated to exploit inter-user interference in non-orthogonal channel estimation and NOMA, respectively. In each phase, the process was performed into two steps, i.e., maximizing the secrecy rate and minimizing the total consumption power. In [33], the authors considered the massive MIMO–NOMA networks in the presence of a passive eavesdropper. To secure the downlink transmission, the BS exploited its knowledge of CSI to precode the confidential information and inject the AN. Moreover, two optimization problems, namely, maximizing the sum ergodic secrecy rate and maximizing the EE were formulated and solved via alternating optimization and difference of convex programming. It was shown that massive MIMO–NOMA outperforms massive MIMO–OMA in terms of sum ergodic secrecy rate and EE. In addition, the work [34] is an extension of [33], where the single-antenna eavesdropper was replaced with a multi-antenna eavesdropper. Presented results revealed that the secrecy rates of cell-center users were significantly enhanced, while cell-edge users could achieve secure communications by employing massive MIMO. Note that the works [32–34] only apply to MRT precoding. In [35], zero-forcing precoding was adopted for a massive MIMO–NOMA network with a passive eavesdropper. It was shown that the zero-forcing precoder could enhance the secrecy performance when compared with the MRT precoder.

19.5 Conclusion In this chapter, we first presented the basic principle of NOMA and showed the superiority of NOMA over OMA in terms of SE. Then, we introduced the fundamentals of PLS, focusing on the concepts of secrecy rate and SOP. On this basis, we elaborated various PLS-assisted NOMA systems in three parts, namely, SISO–, MIMO–, and massive MIMO–NOMA systems. For each part, a detailed introduction on the state-of-the-art research progress was given. For example, for SISO–NOMA systems, works focusing on the secrecy rate maximization, e.g., sum secrecy rate maximization and max-min secrecy rate, as well as SOP minimization were reviewed. Moreover,

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the combination of SISO–NOMA with SWIPT, relay, and FD was introduced. For MIMO–NOMA systems, it was shown that multiple antennas at the BS can be used for TAS, beamforming or generating AN. Meanwhile, the scenarios when MIMO– NOMA is combined with relay and mmWave drone were presented. For massive MIMO–NOMA systems, both MRT and zero-forcing were considered. Moreover, uplink training and downlink transmission should be jointly optimized to improve the system performance. Based on the existing works, PLS in various NOMA systems can be guaranteed via appropriate resource allocation. Nonetheless, it is worth mentioning that some widely adopted assumptions in the literature may be invalid in practice. For example, the assumption that the eavesdropper can perform SIC to remove inter-user interference may overestimate its capability. Indeed, if SIC can be successfully performed at the eavesdropper, it means that the information integrity has already been compromised. Therefore, the results obtained under this assumption may underestimate the system performance. In contrast, the assumption that the BS knows the CSI of the eavesdroppers may overestimate the system performance, especially in the presence of passive eavesdroppers. How to obtain the CSI of the eavesdroppers remains a serious issue for SISO and MIMO systems. However, it is of less concern for massive MIMO systems, in which the system performance mainly depends on the large-scale pathloss (much easier to obtain and remains unchanged for a longer period) rather than small-scale fading. The research on PLS-assisted massive MIMO–NOMA systems is at an early stage, and represents one promising research direction for PLS-assisted NOMA systems.

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Index

absolute blank subframes (ABSs) 438 activation function 516 active antenna combination (AAC) 286 adaptive jammer 578–9 adaptive symbol transitioned OFDM 102–3 additive white Gaussian noise (AWGN) 33, 104, 179, 208 adjacent subcarrier distance vector (ASDV) 186 air-to-air (A2A) communication 15 air-to-ground (A2G) communication 15 Alamouti code 293 Alamouti-Coded OFDM systems 159 alignment signals, OFDM with 107–12 ALOHA 536 amplifiers 385–6 power amplifier (PA) nonlinearities 7, 48–50 variable gain amplifiers (VGAs) 254 analog baseband beamforming 254 analog beamforming 233–4, 254–5 analog front-end (AFE)-based PHY-authentication 573–4 analog RF beamforming 254 analog-to-digital converters (ADCs) 390 analytical channel models 349 correlation-based models (CBMs) 350 i.i.d. Rayleigh fading channel model 350 Kronecker channel model 350 Weichselberger channel model 350–1 propagation-motivated models 351

finite-scatterer model 351 free-space model 351 tapped delay line model 351–2 virtual channel representation model 352–3 angle coherence time 227 angle-of-arrival (AoA) 206, 262 angle-of-departure (AoD) 206, 227, 262 antenna subset transmission 276 artificially interfering (noise/jamming) signals for PLS concepts, merits, and demerits 559 examples in time, frequency, and space domains 559–62 learned lessons 562 artificial neural networks (ANNs) 493, 516, 518 prediction phase 494 training phase 493 asymmetrically clipped DC biased optical OFDM(ADO-OFDM) 417–18 asymmetrically clipped optical OFDM (ACO-OFDM) 417–18 asynchronicity in mixed numerology frame 91–2 asynchronous transmission, robustness against 187–8 authentication 571 automatic encoding (AE) model 523–4 auxiliary particle filter (APF) 272–3 backpropagation 518 bandwidth (BW) 379–81, 384, 386–8 bandwidth part (BWP) 15, 19–20

614

Flexible and cognitive radio access technologies for 5G and beyond

baseband unit (BBU) 448 base station (BS) 323 basis pursuit (BP) algorithm 294 beamforming 12, 14 evolution of 250 fixed/adaptive beamforming 250 implicit/explicit beamforming 251–2 narrow/wide band beamforming 252 transmit/receive beamforming 250–1 expressing channels in ultra-massive antenna settings with 344–5 beamforming in mmWave frequencies 253 analog beamforming 254–5 beampattern adaptation 260–1 digital beamforming 255 maximum ratio transmission (MRT) 255 minimum mean squared error (MMSE) 255–6 zero-forcing (ZF) 255 hybrid beamforming 256 fully connected hybrid beamforming 257 sub-connected hybrid beamforming 257–60 lens antenna for beamforming 261–3 beam management 263 beam switching 270 frequency selective surfaces (FSSs) 270 low temperature co-fired ceramic module 271 reconfigurable slots on cylindrical cavity 271 Rotman Lens antenna design 271 beam tracking 271–3 challenges and future concepts intelligent reflecting surface (IRS)-based beamforming 277 multi-lens antenna beamforming systems 276–7

pilot contamination in mmWave frequencies 275–6 non-standalone architecture 263–4 security-oriented beamforming techniques 273–4 standalone architecture 264 beam determination 266 beam measurement 265–6 beam reporting 266–7 beam sweeping 265 detection accuracy 267–8 overhead 269 reactiveness 269 beampattern adaptation 260–1 Beer–Lambert Law 387 BER/block error rate (BLER) performance 62 bidirectional LMS (BiLMS) 272 binary phase shift keying (BPSK) 146 bit error rate (BER) performance 31, 88, 183, 212, 288 of Tom’s precoder 104 block diagonalization (BD), 233 carrier frequency offset (CFO) 113, 190 carrierless modulation schemes 414–15 carrier sense multiple access with collision avoidance (CSMA/CA) 419 Cayley unitary transform 289 cellular communications use cases 8–10 centralized coordination 439 centralized radio access network 11–12 channel and noise modeling 386–8 channel-based adaptation and optimization for PLS concepts, merits, and demerits 554–5 examples in time, frequency, and space domains 556–8 learned lessons 558–9 channel codes 389 channel correlated matrix (CCM) 205

Index channel impulse response (CIR) 109, 224 channel modeling 341 channel sparsity and compressed modeling in 5G and B5G 370 channel sparsity aspects 372–3 outstanding challenges 373 pilot reduction through compressive channel sampling 371–2 enhanced 3GPP channel models 363 good channel model for 5G and B5G 343–8 beamforming, expressing channels in ultra-massive antenna settings with 344–5 double-directional 3D coverage, supporting 346 expressing multi-numerology waveform structures for B5G 346–7 high mobility scenarios, expressing 345–6 mathematical and implementation tractability 348 mmWave 343–4 multiple domains, consistency and smoothness in 345 propagation scenarios and environments, expressing a wide variety of 346 reconfigurable intelligent surfaces (RISs), supporting 347–8 THz communications 343–4 ultrahigh carrier frequencies and ultrawide bandwidths, rendering channels with 343–4 ultra-reliable low latency communications (URLLCs), representing channels in 348 IEEE 802.11ay channel model 365–6 IMT-2020 channel model 366–7 machine learning-based channel modeling for 5G and B5G 367–70

615

METIS channel models 363–5 MiWEBA channel model 363 NYUSIM channel model 367 QuaDRiGa/mmMAGIC channel model 365 radio frequency channel models, evolution of 348 analytical channel models 349–53 physical channel models 353–9 standardized channel models 359–62 channel models for massive MIMO 205 correlation-based channel model 205 spatial channel model 205–7 channel quality indicator (CQI) 169, 440 channel response 80 channel state information (CSI) 108, 198, 255, 288, 297, 370, 528, 550 adaption 157 imperfect 203–4 perfect 201–3 channel state information (CSI) acquisition for massive MIMO 213 channel estimation 214 channel correlation-based channel estimation 218–21 channel sparsity-based channel estimation 215–18 channel feedback 222 channel correlation-based channel feedback 224–9 channel partial reciprocity-based channel feedback 229–30 channel sparsity-based channel feedback 222–4 chromaticity control 408 cloud-based radio access network (C-RAN) 448–9 clustering 443 co-channel interference (CCI) 435, 437

616

Flexible and cognitive radio access technologies for 5G and beyond

cognitive radio (CR) technology 15, 482–4, 570 spectrum decision 485 spectrum mobility 486 spectrum sensing 485 spectrum sharing 486 cognitive radio networks, spectrum map in by statistical inference and learning 537–8 cognitive radio spectrum sensing 481 predictive spectrum-sensing approach 492 employed machine-learning methodologies 493–5 state-of-the-art 495–8 quality-of-service (QoS)-aware dynamic spectrum access techniques 498 performance evaluation 502–5 traditional spectrum-sensing techniques narrowband spectrum sensing 486–8 wideband spectrum sensing 488–92 cognitive radio technologies 12 coherence bandwidth of the channel 36 coherence time of the channel 39 color shift keying (CLSK) 414 communication link, securing 190–1 communications stack 3 communication systems, relation between modulation and waveform in 145–6 communication technologies and standards 452–3 complementary cumulative distribution function (CCDF) 35 compressed sensing (CS) theory 285, 294–5 compressive channel sampling, pilot reduction through 371–2 compressive sensing based (CSB) wideband spectrum sensing 491

constant jammer 577 continuous aperture phased MIMO (CAP-MIMO) 263 continuous wavelet transform (CWT) 489 controllable wireless propagation 320 eliminating Doppler effects with RISs 323–6 two-ray propagation with RISs 320–3 conventional and differential digital modulations for OFDM-based waveform 149–50 convolutional neural networks (CNNs) 494–5, 519 coordinated beamforming-CoMP (CB-CoMP) 439 coordinated multipoint (CoMP) 11–12, 438 architecture 439 centralized coordination 439 distributed coordination 439 and centralized RAN technologies 15 implementation 442 clustering 443 user selection and resource allocation 442–3 reference signals and interference measurement 444 scenarios 441 heterogeneous networks 442 inter-site CoMP 442 intra-site CoMP 441 types 439 coordinated scheduling and beamforming 439–40 dynamic point selection-CoMP (DPS-CoMP) 441 joint transmission-CoMP (JT-CoMP) 441 coordinated networks, future backhaul issues 456 functionality split 455–6 performance analysis 456 synchronization/timing advance 455

Index coordinated scheduling and beamforming 439–40 coordinated scheduling-CoMP (CS-CoMP), 439, 441, 447 coordinate interleaved OFDM-IM (CI-OFDM-IM) 183–4 coordination for future wireless networks 448 application and user requirements 454–5 communication technologies and standards 452–3 network architecture 448 cloud-RAN 448–9 fog-RAN 450–1 smart radio environment 451–2 coordination in 5G networks 444 coverage 446 energy efficiency 447–8 mobility 446–7 reliability and latency 445–6 spectral efficiency 447 throughput 445 coordination in legacy networks 435 coordinated multipoint (CoMP) 438 architecture 439 implementation 442–3 reference signals and interference measurement 444 scenarios 441–2 types 439–41 enhanced intercell interference coordination 438 frequency reuse 436–7 intercell interference coordination 437–8 correlation-based channel model 205 correlation-based models (CBMs) 349–50 i.i.d. Rayleigh fading channel model 350 Kronecker channel model 350 Weichselberger channel model 350–1

617

COST channel models (259 and 273) 359 covariance-based detection (CBD) technique 487–8 cumulative distribution function (CDF) 201 cyclic prefix (CP) 14, 32, 36–7, 45 cyclic prefix (CP)-DFT-s-OFDM 59–60 cyclic prefix (CP)-OFDM 31 impairments, performance with 40 frequency offset 41–3 in-phase and quadrature (IQ) impairments 50–2 phase noise 47–8 power amplifier (PA) nonlinearities 48–50 sampling clock offset 46 symbol timing error 43–5 key features 31–5 multipath channel, performance in 35 frequency-dispersive multipath channel 39–40 time-dispersive multipath channel 36–9 cyclostationary detection (CD) method 487 data center network (DCN) 425 data centers 425–6 data-driven prediction using deep learning 520 automatic encoding (AE) model 523–4 meteorology data set 522 multivariate long and short term memory (MLSTM) model 524–5 overfitting, method to prevent 525 points of interest data set 522 spatial prediction of trip demand 522–3 taxi trajectory data set 521–2

618

Flexible and cognitive radio access technologies for 5G and beyond

temporal prediction and multivariate long and short term memory model 523 data rates, achieved 391 DC biased optical OFDM (DCO-OFDM) 417 deep fading, avoiding 188–9 deep learning (DL) 494, 513, 515–16 convolutional neural networks (CNNs) 494–5 long and short term memory (LSTM) 494 deep learning (DL) for wireless communication systems and networks 516 artificial neural network basics 516–19 data-driven prediction using deep learning 520 automatic encoding (AE) model 523–4 meteorology data set 522 method to prevent overfitting 525 multivariate long and short term memory (MLSTM) model 524–5 points of interest data set 522 spatial prediction of trip demand 522–3 taxi trajectory data set 521–2 temporal prediction and multivariate long and short term memory model 523 digital communication systems, DL for signal detection in 526–8 future network architect of machine learning 528 machine learning in mobile communication networks 528–30 networked multi-agent systems 530–1 deep neural network (DNN) 518 delay spreads (DSs) 114 detection accuracy 267–8

deterministic models 358–9 device-to-device (D2D) communication 342, 345, 450 differential amplitude PPM (DAPPM) 415 differential PPM (DPPM) 415 differential spatial modulation 288–91 digital beamforming 255 maximum ratio transmission (MRT) 255 minimum mean squared error (MMSE) 255–6 zero-forcing (ZF) 255 digital communication systems, DL for signal detection in 526–8 digital precoding 231 multiuser digital precoding 232–3 single-user digital precoding 231–2 digital subscriber line (DSL) 414 digital-to-analog converter (DAC) 35 direct detection (DD) 413 direction of arrival (DoA) 250 Dirichlet sinc function 59 discrete Fourier transform (DFT) transformation 215, 222, 229 discrete Fourier transform spread OFDM (DFT-s-OFDM) signal 56 discrete wavelet transform (DWT) 489 distributed artificial intelligence (DAI) 530 distributed coordination 439 distributed problem solving (DPS) 530 distributed scattering model 355 Doppler effect 62, 318 Doppler effects elimination with RISs 323–6 Doppler shifts 15, 39, 323–4 Doppler spread 6, 39 avoiding 189–90 double-directional CIR (DDCIR) 359 double-directional 3D coverage, supporting 346 double-ring model 356–7 dual-mode OFDM (DM-OFDM) 182–3

Index dynamic clustering 440 dynamic point selection-CoMP (DPS-CoMP) 441 Earth exploration-satellite service (EESS) 382 electromagnetic (EM) lens 261 electromagnetic (EM) signal 250 energy detector (ED) approach 487 energy harvesting 448 enhanced mobile broadband (eMBB) 8, 14, 20, 29, 67, 89, 122, 165, 175, 192, 264, 444 enhanced SIM-OFDM (ESIM-OFDM) 176 enhanced unipolar OFDM (EU-OFDM) 418 entertainment technologies and augmented reality 383 equal bit protection 185–7 Euclidean distance 103 evolved packet core (EPC) 264 expurgated PPM (EPPM) 415–16 extended KF (EKF) 272 extended Saleh–Valenzuela model 357 fading 320 fast Fourier transform (FFT) process 33, 113, 179, 474 favorable propagation conditions 200 featured modulation options for 5G and beyond networks 165–6 federate learning 531 basics 531–2 over multiple access communications 536–7 over wireless networks 534–6 through wireless communications 533–4 FedSGD algorithm 533 fifth generation (5G) new radio standardization 15 bandwidth part issues 19–20 comparison for building blocks of 5G NR and LTE 21–3

619

5GC 265 numerology structures 17–19 slot structures 20–1 3GPP, reference documents for 16–17 filter-bank based (FBB) MB spectrum sensing 490 filter bank multicarrier (FBMC) 53–5, 62, 100, 168 filter bank multicarrier (FBMC)-offset quadrature amplitude modulation (OQAM) 54 filtered multitone (FMT) 117 filtered-orthogonal frequency division multiplexing (F-OFDM) 58–9, 168 finite impulse response (FIR) filter 57 finite-scatterer model 351 fixed/adaptive beamforming 250 flickering 408 flying base stations (FBSs) 451 fog access points (F-AP) 450 fog-RAN 450–1 fog UEs (F-UEs) 450 forward error correction (FEC) codes 188, 389 fractional frequency reuse (FFR) 436 fractional numerology domains (FNDs) 131 frame structure 5 free-space model 351 free space optical (FSO) 404 free-space propagation model 321 frequency-dispersive multipath channel 39–40 frequency division duplex (FDD) system 550 frequency division duplexing (FDD) massive MIMO system 215, 229 frequency domain 70–1 index modulation in 176 log-likelihood ratio detector 180 maximum likelihood detector 180

620

Flexible and cognitive radio access technologies for 5G and beyond

frequency domain equalizer (FDE) 33, 37, 62 frequency-modulated continuous wave (FMCW) 68 frequency offset 41–3 frequency selective surfaces (FSSs) 270 full-featured optimisation (FFO) 501 fully connected hybrid beamforming 257 Gaussian distribution 34, 200 Gaussian waveform 119 Gauss–Seidel method 212 generalized and flexible modulation options 143 communication systems, relation between modulation and waveform in 145–6 conventional and differential digital modulations for OFDM-based waveform 149–50 featured modulation options, applications of 165–6 flexibility in modulation design 146–7 futuristic modulation options for beyond 5G 169–70 index-based modulation options 151 OFDM with index modulation (OFDM-IM) scheme 154–5 spatial modulation-OFDM (SM-OFDM) scheme 152–4 multi-dimensional modulation options for OFDM-based waveform 150–1 number-based modulation options 155–7 performance evaluation and comparison of modulation options 159 computational complexity 164 out-of-band leakage 164 peak-to-average power ratio (PAPR) and power efficiency 162–4

reliability 161–2 spectral efficiency (SE) 159–61 potential flexible modulation options for OFDM-based waveforms 166–8 shape-based modulation options 157–9 generalized frequency division multiplexing (GFDM) 55–7, 62, 159 generalized OFDM-IM 181–2 generalized space frequency index modulation-OFDM (GSFIM-OFDM) 149 generalized spatial modulation 285–8 generative adversarial network (GAN)-based approach 370 geometry-based stochastic models (GSCMs) 353 distributed scattering model 355 double-ring model 356–7 single-ring model 355–6 global positioning system (GPS) receiver 571 grant-free (GF) random access techniques 470 adaptive resource utilization 472–3 transmission schemes 470 K-Repetitions 471 proactive transmission 471 reactive transmission 470 Grassmannian codebook 223 hardware imperfection, robustness against 190 Hermite–Gaussian (HG) carriers 144 Hermitian symmetry 417 heterogeneous networks (HetNets) 117, 119, 383, 438 hidden Markov models (HMMs) 493, 495–6 high electron mobility transistor (HEMT) 384 high interference indicator (HII) 437

Index high mobility scenarios, expressing 345–6 high-speed vehicle-to-everything (V2X) communication 189 horizontal federated learning (HFL) 532 hospitals 424 hybrid asymmetrically clipped optical OFDM (HACO-OFDM) 416 hybrid beamforming 256 fully connected hybrid beamforming 257 sub-connected hybrid beamforming 257–60 hybrid precoding 234 multiuser hybrid precoding 239–41 single-user hybrid precoding 234–8 hybrid waveforms, flexibility through 99 numerology-based scheduling 126–35 partially overlapping waveforms 117–20 secure OFDM 112–16 spectrally localized OFDM 101 adaptive symbol transitioned OFDM 102–3 OFDM with alignment signals 107–12 partial transmit sequenced OFDM 105–7 precoded OFDM 103–5 waveform multiplexing approaches for beyond 5G RATs 120 FDM of OFDM numerologies against hybrid waveforms 122–6 time-domain OFDM numerology multiplexing 120–1 i.i.d. Rayleigh fading channel model 350 imperfect channel state information 203–4 implicit/explicit beamforming 251–2

621

IMT-2020 channel model 366–7 IMT-advanced channel models from ITU 362 index-based modulation options 151 OFDM with index modulation (OFDM-IM) scheme 154–5 spatial modulation-OFDM (SM-OFDM) scheme 152–4 index modulated OFDM spread spectrum (IM-OFDM-SS) 149 index modulation-based flexible waveform design 175 frequency domain, index modulation in 176 log-likelihood ratio detector 180 maximum likelihood detector 180 future directions 191 state-of-the-art OFDM-IM solutions 180 coordinate interleaved OFDM-IM 183–4 dual-mode OFDM 182–3 generalized OFDM-IM 181–2 interleaved OFDM-IM 181 subcarrier activation ratio 189 avoiding Doppler spread 189–90 robustness against hardware imperfection 190 securing communication link 190–1 subcarrier mapping scheme 185 avoiding deep fading 188–9 equal bit protection 185–7 robustness against asynchronous transmission 187–8 index modulation in non-orthogonal multiple accessing 469 indoor light propagation 409–11 industrial Internet of Things (IoT) 9 industries 424–5 infrared (IR) and VLC, integration of 421 inner-subcarrier activation (ISA) 187 in-phase and quadrature imbalance (IQI) 190

622

Flexible and cognitive radio access technologies for 5G and beyond

in-phase and quadrature (IQ) impairments 50–2 Institute of Electronics and Electrical Engineers 802.11ay channel model 365–6 intelligent jammer 579 intelligent reflecting surface (IRS)-based beamforming 277 intelligent reflecting surface (IRS)-based massive MIMO 243–4 intensity modulation 413 inter-carrier interference (ICI) 6–7, 39–43, 82, 113, 117, 190 inter-cell interference coordination (ICIC) 436–8 enhanced 438 interleaved OFDM-IM 181 intermittent jammer 577–8 international mobile telecommunications-advanced (IMT-A) channel model 362 International Telecommunication Union (ITU) 29, 404 Internet of Things (IoT) 113, 568–9 inter-numerology interference (INI) management 80, 100, 476 INI-aware guard band allocation 88–91 INI-aware scheduling 86–8 restructuring INI through common CP 80 INI analysis with common CP 82–6 inter-numerology interference (INI) modeling 73–6 factors affecting 76 channel response 80 power offset 77–80 subcarrier spacing ratio 76–7 inter-site CoMP 442 inter-symbol interference (ISI) 31, 82, 114, 117, 411, 526 inter-user interference (IUI) 187, 462, 475

intra- and inter-frame flicker mitigation technologies 408 intra-site CoMP 441 inverse fast Fourier transform (IFFT) 31, 34, 71, 147, 179, 417 iteration-based signal detection 211–13 Japan Electronics and Information Technology Industries Association (JEITA) 406 joint orthogonal match pursuit (JOMP) algorithm 217–18 joint-radar communications 11 joint transmission-CoMP (JT-CoMP) 441 Kalman filter (KF)-based tracking algorithm 271 Karhunen–Loeve representation 205 key performance indicators (KPIs) 11, 13, 456 K-Repetitions 471 Kronecker channel model 350 Kronecker model 349 latency-focused optimisation (LFO) 501 lattice structure 5 layered ACO-OFDM (LACO-OFDM) 416 least common multiplier (LCM) symbol duration 70 least mean square (LMS) algorithm 272 least squares estimate (LSE) method 215 lens antenna array (LAA) 261 lens antenna for beamforming 261–3 light fidelity (Li-Fi) 421 line-of-sight (LOS) and non-line-of-sight (NLOS) channel models 411–13 link-adaptive spatial modulation 292 logistic function 517 log-likelihood ratio (LLR) detector 180, 183, 185–6, 188

Index long and short term memory (LSTM) 494, 496–7 Long Term Evolution (LTE) 5, 8, 23 loss function 515 low-density parity-check (LDPC) codes 553–4 low-density spreading orthogonal frequency division multiple access 467–8 low temperature co-fired ceramic module 271 Lund massive MIMO (LuMaMi) prototype 242 machine learning (ML) techniques 483, 513 future network architect of 528 in mobile communication networks 528–30 networked multi-agent systems 530–1 machine learning-based channel modeling for 5G and B5G 367–70 M -ary phase shift keying (PSK) 143 M -ary quadrature amplitude modulation (QAM) 143 massive machine type communication (mMTC) 8, 13–14, 29, 67, 78, 165, 175, 192, 342, 344, 444 massive multiple-input multiple-output (mMIMO) systems 197, 318 challenges and future research directions for 242 intelligent reflecting surface (IRS)-based massive MIMO 243–4 physical layer signal processing in wideband massive MIMO 242–3 terahertz (THz) massive MIMO 243 channel models for 205

623

correlation-based channel model 205 spatial channel model 205–7 channel state information (CSI) acquisition for 213 channel estimation 214–21 channel feedback 222–30 fundamental of 198–201 physical layer security (PLS) in 567–8, 605 precoding for 230 analog beamforming 233–4 digital precoding 231–3 hybrid precoding 234–41 prototype and testbeds for 241–2 signal detection for 207 iteration-based signal detection 211–13 Neumann sequence-based signal detection 209–10 system model and MMSE detection 207–9 spectrum efficiency analysis of 201 imperfect CSI 203–4 perfect CSI 201–3 matched filter (MF) technique 487 mathematical and implementation tractability 348 maximum Doppler shift 39 maximum likelihood detection 180, 201, 207 maximum ratio combining (MRC) 201, 560 maximum ratio transmission (MRT) 255 media-based modulation (MBM) 149, 169–70 medium access control (MAC) 3, 6, 390, 419–20 metasurfaces 319 METIS channel models 363 METIS hybrid model 365 METIS map-based model 364 METIS stochastic model 364

624

Flexible and cognitive radio access technologies for 5G and beyond

millimeter-wave (mmWave) frequencies 14, 253, 343–4 analog beamforming 254–5 bands 11 beampattern adaptation 260–1 communications 318 digital beamforming 255 maximum ratio transmission (MRT) 255 minimum mean squared error (MMSE) 255–6 zero-forcing (ZF) 255 hybrid beamforming 256 fully connected hybrid beamforming 257 sub-connected hybrid beamforming 257–60 lens antenna for beamforming 261–3 physical layer security (PLS) in 566–7 spatial modulation in mmWave communications 298–9 Millimeter-Wave Evolution for Backhaul and Access (MiWEBA) channel model 363 minimum mean square error (MMSE) 201, 207–9, 255–6 mini-slots 20 mixed numerology OFDM and interference issues 67 asynchronicity in mixed numerology frame 91–2 inter-numerology interference (INI), factors affecting 76 channel response 80 power offset 77–80 subcarrier spacing ratio 76–7 inter-numerology interference (INI) management 80 INI-aware guard band allocation 88–91 INI-aware scheduling 86–8 restructuring INI through common CP 80–6

inter-numerology interference (INI) modeling 73–6 multiplexed numerologies 70 frequency domain 70–1 time domain 71–2 single-carrier (SC) schemes, mixed numerology in 92 mobile communication networks, machine learning in 528–30 mobile network operators (MNOs) 481 mobile station (MS) 323 model-free ML techniques 515 modulation 145 flexibility in modulation design 146–7 relation between modulation and waveform in communication systems 145–6 modulation options for 5G and beyond waveforms 147 conventional and differential digital modulations for OFDM-based waveform 149–50 multi-dimensional modulation options for OFDM-based waveform 150–1 performance evaluation and comparison of 159 computational complexity 164 out-of-band leakage 164 peak-to-average power ratio (PAPR) and power efficiency 162–4 reliability 161–2 spectral efficiency (SE) 159–61 modulation schemes 388–9 molecular communication (MC) 302 spatial modulation -based 302–3 multi-agent system (MAS) 530 multiband (MB) spectrum sensing 489–90 multi-carrier modulation schemes 31, 416–18 multicarrier schemes

Index filter bank multicarrier (FBMC) 53–5 filtered-orthogonal frequency division multiplexing (F-OFDM) 58–9 generalized frequency division multiplexing (GFDM) 55–7 universal filtered multicarrier (UFMC) 57–8 windowed-orthogonal frequency division multiplexing (W-OFDM) 52–3 multichannel sub-Nyquist (MCSN) wideband spectrum sensing methods 491 multicolor modulation schemes 418–19 multi-dimensional modulation options for OFDM-based waveform 150–1 multidimensional parametric channel model 360 multi-input multi-output (MIMO) 12, 14 multi-layer perceptron (MLP) algorithm 493, 496–7 multi-lens antenna beamforming systems 276–7 multi-numerology waveform structures, expressing for B5G 346–7 multipath channel, performance of OFDM in 35 frequency-dispersive multipath channel 39–40 time-dispersive multipath channel 36–9 multiple access communications, federated learning over 536–7 multiple domains, consistency and smoothness in 345 multiple-input multiple-output (MIMO) systems 31, 169–70, 175, 250, 386, 389–90 MIMO–NOMA systems, physical layer security (PLS) in 602–4

625

multiple-input single-output (MISO) system 297 multiple-mode OFDM (MM-OFDM) 183 multiple orthogonal frequency division multiplexing (OFDM) numerologies 318 multi-pulse PPM (MPPM) 415–16 multi-symbol encapsulated OFDM (MSE-OFDM) 81 multi-user detection (MUD) 294, 462 multiuser digital precoding 232–3 multiuser hybrid precoding 239–41 multivariate long and short term memory (MLSTM) model 523–5 nano devices 382–3 narrowband spectrum sensing 486 limitations 488 methodologies 487 covariance-based detection (CBD) technique 487–8 cyclostationary detection (CD) method 487 energy detector (ED) approach 487 matched filter (MF) technique 487 narrow subcarrier numerology (NSN) 70–2, 82–5 narrow/wide band beamforming 252 Nash equilibrium 119 network architecture 448 cloud-RAN 448–9 fog-RAN 450–1 networked multi-agent systems 530–1 Neumann sequence-based signal detection 209–10 Neumann series approximation 209 New Radio (NR) 265 NR-Light 9 new radio resource block 20 New York University open source 5G channel simulation software (NYUSIM) channel model 367

626

Flexible and cognitive radio access technologies for 5G and beyond

noise-plus-interference (NPI) 208 noise sources in THz bands 387–8 non-geometry-based stochastic models 357 extended Saleh–Valenzuela model 357 Zwick model 357–8 non-line-of-sight (NLOS) communication 380, 404 non-orthogonal multiple access (NOMA) 447, 591 downlink NOMA 591–5 physical layer security (PLS)-enhanced 599 massive multiple-input multiple-output (mMIMO)–NOMA systems 605 multiple-input multiple-output (MIMO)–NOMA systems 602–4 single-input single-output (SISO)–NOMA systems 599–602 uplink NOMA 595–6 non-orthogonal multiple access (NOMA)-aided spatial modulation 295–6 non-orthogonal radio access technologies 461 future directions 477–8 grant-free random access techniques 470 adaptive resource utilization 472–3 transmission schemes 470–1 non-orthogonal multiple accessing in power domain 462 capacity in PD-NOMA 464–6 downlink PD-NOMA 462–3 fairness in PD-NOMA 466–7 uplink PD-NOMA 463–4 state-of-the-art NOMA solutions 467 index modulation in NOMA 469

low-density spreading orthogonal frequency division multiple access 467–8 pattern division multiple access (PDMA) 468–9 waveform coexistence for multiple accessing 473 OFDM with multi-numerology 476–7 OFDM with OFDM-IM 474–6 wideband and narrowband signals 473–4 non-terrestrial transmission points 12, 15 number-based modulation options 155–7 numerology-based scheduling 126–35 Nyquist-based approaches 489 filter-bank based (FBB) MB spectrum sensing 490 multiband (MB) spectrum sensing 489–90 wavelength transform based (WTB) spectrum sensing 489 Nyquist rate 390–1 Nyquist–Shannon sampling theorem 488 offset quadrature amplitude modulation (OQAM)-FBMC 54, 56 on–off keying (OOK) signals 389, 415 open air interface (OAI) massive MIMO testbed 242 Open System Interconnection layers 126 optical code division multiple access (O-CDMA) 419 optical modulation schemes 413 carrierless modulation schemes 414–15 multi-carrier modulation schemes 416–18

Index multicolor modulation schemes 418–19 single-carrier modulation schemes 415–16 optical non-orthogonal multiple access (O-NOMA) 419 optical orthogonal frequency division multiple access (OFDMA) 419 optical-orthogonal multiple access (OMA) 419 optical single carrier frequency division multiple access (O-SCFDMA) 419 optical wireless communication (OWC) 299, 403–4 optical wireless communications, spatial modulation in 299–301 ordered block minimum mean-squared-error (OB-MMSE)-based detections 287 orthogonal frequency division multiple access (OFDMA) system 29, 117, 420, 452, 461 orthogonal frequency division multiplexing (OFDM) 16, 99, 101, 143, 301, 413, 416–17 OFDM-aided space–time shift keying (OFDM-STSK) 149 OFDM-differential modulation (OFDM-DM) 144 OFDM with adaptive IM and adaptive constellation modulation (OFDM-AIM-ACM) 191 OFDM with adaptive IM and fixed constellation modulation (OFDM-AIM-FCM) 191 OFDM with index modulation (OFDM-IM) scheme 112, 144, 149, 154–5, 163–4, 166 OFDM with pulse superposition modulation (OFDM-PSM) 144, 149 OFDM with subcarrier index selection (OFDM-SIS) 190, 556

627

OFDM with subcarrier number modulation (OFDM-SNM) 144, 149, 163–4, 166 OFDM with subcarrier power modulation (OFDM-SPM) 167 partially overlapping waveforms 117–20 secure OFDM 112–16 spectrally localized OFDM 101 adaptive symbol transitioned OFDM 102–3 OFDM with alignment signals 107–12 partial transmit sequenced OFDM 105–7 precoded OFDM 103–5 orthogonal frequency division multiplexing (OFDM) and alternative waveforms 29 cyclic prefix (CP)-OFDM 31 impairments, performance with 40–52 key features 31–5 multipath channel, performance in 35–40 multicarrier schemes filter bank multicarrier (FBMC) 53–5 filtered-orthogonal frequency division multiplexing (F-OFDM) 58–9 generalized frequency division multiplexing (GFDM) 55–7 universal filtered multicarrier (UFMC) 57–8 windowed-orthogonal frequency division multiplexing 52–3 single-carrier schemes CP-DFT-s-OFDM 59–60 unique word-DFT-s-OFDM (UW DFT-s-OFDM) 61–2 ZT-DFT-s-OFDM 60–1 orthogonal frequency division multiplexing (OFDM)-based waveform 68–9

628

Flexible and cognitive radio access technologies for 5G and beyond

conventional and differential digital modulations for 149–50 multi-dimensional modulation options for 150–1 potential flexible modulation options for 166–8 orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM) 176–7 coordinate interleaved 183–4 dual-mode OFDM 182–3 generalized 181–2 interleaved 181 log-likelihood ratio detector 180 maximum likelihood detector 180 subcarrier activation ratio 189 avoiding Doppler spread 189–90 robustness against hardware imperfection 190 securing communication link 190–1 subcarrier mapping scheme 185 avoiding deep fading 188–9 equal bit protection 185–7 robustness against asynchronous transmission 187–8 orthogonal multiple access (OMA) 461, 589 orthogonal time frequency and space (OTFS) 170 orthogonal transform division multiplexing (OTDM) waveform 556 out-of-band emission (OOBE) 7, 14–15, 31, 53, 61, 101, 103–6, 109–10, 187 out-of-band leakage 164 overhead 269 overlapping PPM (OPPM) 415–16 overload indicator (OI) 437 parallel-to-serial (P/S) blocks 71 partially overlapping waveforms 117–20

partial transmit sequenced OFDM 105–7 partial transmit sequences (PTS) 105 pattern division multiple access (PDMA) 468–9 peak-to-average power ratio (PAPR) 14, 31, 34, 61, 81, 92, 101, 105, 109–10, 416, 559 and power efficiency 162–4 perfect channel state information 201–3 phase noise 47–8 phase shifter (PS) 230, 252, 254 phase-shift keying (PSK) 143 photodiodes 406, 408 physical channel models 353 deterministic models 358–9 geometry-based stochastic models (GSCMs) 353 distributed scattering model 355 double-ring model 356–7 single-ring model 355–6 non-geometry-based stochastic models 357 extended Saleh–Valenzuela model 357 Zwick model 357–8 physical layer (PHY)-authentication 570 analog front-end (AFE)-based PHY-authentication 573–4 channel-based PHY-authentication 572 complex heterogeneous networks, efficient and fast authentication in 574 prediction of PHY-attributes 574–5 security context sharing 575–6 integration with the existing network infrastructure and authentication protocols 576 reliability of 574 physical layer (PHY) technologies 4, 318

Index physical layer security (PLS) 112–13, 190, 273, 296, 545, 597 applications 565 in cognitive radio (CR) systems 570 in Internet of Things (IoT) 568–9 in massive multiple-input multiple-output (mMIMO) 567–8 in millimeter-wave (mmWave) 566–7 in ultra-reliable low latency communication (URLLC) 568 in unmanned aerial vehicle (UAV) 569–70 challenges and future research directions 580 challenges related to solution against jamming attacks 582 cross-layer security design 580 hybrid security techniques 581 impersonation attacks 582 joint design of secrecy, throughput, delay, and reliability 581 line-of-sight (LOS) environment, security in 580–1 mixed attacks in wireless networks and cognitive security 582–3 new direction for PLS 583 peak-to-average power ratio (PAPR) of AN-based and precoding security techniques 580 robust channel estimation and channel reciprocity calibration 581 secrecy design based on service requirements 580 fundamentals, preliminaries, and basic system model for 549–50 information-theoretic secrecy 597–8 in massive multiple-input multiple-output–NOMA systems 605 metrics

629

ergodic secrecy capacity 598 secrecy outage probability 599 in multiple-input multiple-output (MIMO)–NOMA systems 602–4 physical layer (PHY)-authentication 570 analog front-end (AFE)-based PHY-authentication 573–4 channel-based PHY-authentication 572 complex heterogeneous networks, efficient and fast authentication in 574–6 integration with the existing network infrastructure and authentication protocols 576 reliability of 574 secrecy notions 551 ideal secrecy 552 perfect secrecy 551 semantic secrecy 552 strong secrecy 551 weak secrecy 551–2 secrecy performance metrics 552–3 security techniques, popular 553 addition of artificially interfering (noise/jamming) signals for PLS 559–62 channel-based adaptation and optimization for PLS 554–9 extraction of secret sequences from wireless channels 563–5 secure channel coding design, PLS based on 553–4 in single-input single-output (SISO)–NOMA systems 599–602 wireless jamming attacks 576 adaptive jammer 578–9 constant jammer 577 intelligent jammer 579 intermittent jammer 577–8 reactive jammer 578

630

Flexible and cognitive radio access technologies for 5G and beyond

physical layer signal processing in wideband massive MIMO 242–3 pilot contamination in mmWave frequencies 275–6 pilot reduction through compressive channel sampling 371–2 plasmonic transmit-receive arrays 327 points of interest (POI) score 520 polar codes 553 post-massive MIMO (PM-MIMO) 454 power amplifier (PA) nonlinearities 7, 48–50 power difference-based (PDB) scheduling 133 power-domain non-orthogonal multiple accessing (PD-NOMA) 461 capacity in 464–6 downlink 462–3 fairness in 466–7 uplink 463–4 power domain O-NOMA (PDO-NOMA) 420 power line communication (PLC) and VLC, integration of 421 power offset 77–80 power spectral density (PSD) 33, 103–4, 125 precoded OFDM 103–5 precoding-aided SM 291 precoding for massive MIMO 230 analog beamforming 233–4 digital precoding 231 multiuser digital precoding 232–3 single-user digital precoding 231–2 hybrid precoding 234 multiuser hybrid precoding 239–41 single-user hybrid precoding 234–8 precoding matrix indicator (PMI) 440 precoding/TCM-aided spatial modulation 292–3

predictive spectrum-sensing approach 492 employed machine-learning methodologies 493 artificial neural networks 493–4 deep learning 494–5 hidden Markov models (HMMs) 493 state-of-the-art 495–8 primary user emulation attack (PUEA) 570 proactive GF transmission 471 probability mass function (PMF) 526 propagation-motivated models 349, 351 finite-scatterer model 351 free-space model 351 tapped delay line model 351–2 virtual channel representation model 352–3 propagation scenarios and environments, expressing a wide variety of 346 pulse amplitude modulation (PAM) 413 pulse position modulation (PPM) 303, 413 pulse shape 5 quadratically constrained quadratic program 106 quadrature amplitude modulation (QAM) 34, 143, 388 QuaDRiGa/mmMAGIC channel model 365 quality-of-service (QoS)-aware dynamic spectrum access techniques 498 performance evaluation 502–5 quality-of-service (QoS) requirements 318, 485, 547 quantum cascade lasers (QCLs) 385 radio access network (RAN) 11, 448 radio access technologies (RATs) 3, 11, 67, 135, 501

Index limitations and challenges for 11–12 performance indicators for waveform design 12–13 waveform design guidelines for 14 beamforming 14 cognitive radio 15 coordinated multipoint (CoMP) and centralized RAN technologies 15 millimetre-wave frequencies 14 multi-input multi-output (MIMO) 14 non-terrestrial transmission points 15 radio frequency (RF) chain output 254 front-end components 32 integration of RF and VLC 421 radio frequency channel models, evolution of 348 analytical channel models 349 correlation-based models (CBMs) 350–1 propagation-motivated models 351–3 physical channel models 353 deterministic models 358–9 geometry-based stochastic models 353–7 non-geometry-based stochastic models 357–8 standardized channel models 359 3GPP spatial channel model 360–1 COST channel models (259 and 273) 359 IMT-advanced channel models from ITU 362 multidimensional parametric channel model 360 WINNER channel model 361–2 random access channel (RACH) 264 random vector quantization (RVQ) 223 Rayleigh fading 159, 200, 349, 462 ray-tracing-based approach 386

631

reactive GF transmission 470 reactive jammer 578 reactiveness 269 receive SM (RSM) 169 receive spatial modulation 291 reconfigurable antennas (RAs) 120 reconfigurable intelligent surface (RIS) 11–12, 451, 557 supporting 347–8 reconfigurable intelligent surface (RIS)-assisted wireless communications 317 controllable wireless propagation 320 eliminating Doppler effects with RISs 323–6 two-ray propagation with RISs 320–3 future perspectives 331–2 literature survey 326–30 potential use-cases 330 increasing PHY security 331 low-energy IoT 331 non-line-of-sight (NLOS) transmission, supporting 330–1 reducing Doppler and fading effects 331 simple transceivers 331 reconfigurable slots on cylindrical cavity 271 recurrent neural network (RNN) 494, 519 reference signal (RS) 250 reference signal received power (RSRP) 447 reference signal received quality (RSRQ) 447 regression function, optimal 515 reinforcement learning 483 relative narrowband transmitted power (RNTP) 437 relay-assisted interference techniques 561 remote radio heads (RRHs) 441–2, 448 resource elements (REs) 125

632

Flexible and cognitive radio access technologies for 5G and beyond

robustness against asynchronous transmission 187–8 robustness against hardware imperfection 190 root mean square (RMS) delay spread 413 Rotman lens 262 Rotman Lens antenna design 271 Saleh’s PA model 49 sample timing offset (STO) 8 sampling clock offset 46 secondary reference signal (SRS) 265–6 secrecy notions 551 ideal secrecy 552 perfect secrecy 551 semantic secrecy 552 strong secrecy 551 weak secrecy 551–2 secrecy performance metrics 552–3 secure communication 426 security-oriented beamforming techniques 273–4 security provisioning in spatial modulation 296–8 security techniques, popular 553 addition of artificially interfering (noise/jamming) signals for PLS concepts, merits, and demerits 559 examples in time, frequency, and space domains 559–62 learned lessons 562 channel-based adaptation and optimization for PLS concepts, merits, and demerits 554–5 examples in time, frequency, and space domains 556–8 learned lessons 558–9 secure channel coding design, PLS based on concepts, merits, and demerits 553–4 learned lessons 554

wireless channels, extraction of secret sequences from concepts, merits, and demerits 563 examples in time, frequency, and space 564–5 learned lessons 565 serial-to-parallel (S/P) blocks 71 shape-based modulation options 157–9 signal detection for massive MIMO 207 iteration-based signal detection 211–13 Neumann sequence-based signal detection 209–10 system model and minimum mean square error (MMSE) detection 207–9 signal-to-interference-plus-noise ratio (SINR) maximization 328 signal-to-interference ratio (SIR) 78–9 signal-to-noise ratio (SNR) 47, 150, 287, 328 signal-to-phase-noise-interference-ratio (SPNIR) 47 simultaneous wireless information and power transfer (SWIPT) system 291, 301–2, 448 single-carrier (SC) schemes CP-DFT-s-OFDM 59–60 mixed numerology in 92 modulation schemes 415–16 unique word-DFT-s-OFDM (UW DFT-s-OFDM) 61–2 ZT-DFT-s-OFDM 60–1 single-input single-output (SISO)–NOMA systems physical layer security (PLS) in 599–602 single-RF spatial modulation 283–5 single-ring model 355–6 single-user digital precoding 231–2 single-user hybrid precoding 234–8 singular value decomposition (SVD) 205 sixth generation (6G) wireless networks 317

Index small cell transmission points 14 smart radio environment 451–2 Snell’s law 322–3 soft frequency reuse (SFR) 437 software-controlled hypersurfaces 327 software-defined networking (SDN) 449 solid state lighting (SSL) source 407 space division multiple access schemes 419 space-shift keying (SSK) system 284, 300 space–time block code (STBC) 293 space–time shift keying (STSK) modulation scheme 289 sparse code multiple access (SCMA) 468 spatial channel model 205–7 spatial modulation (SM) techniques 283, 326 -based molecular communication 302–3 -based simultaneous wireless information and power transfer 301–2 basic principle and variants of 283 differential SM 288–91 generalized SM 285–8 receive SM 291 single-RF SM 283–5 generalized SM integration with other promising technologies compressed-sensing (CS) theory for SM 294–5 non-orthogonal multiple access (NOMA)-aided SM 295–6 security provisioning in SM 296–8 in mmWave communications 298–9 in optical wireless communications 299–301 performance enhancement for 292 link-adaptive SM 292 precoding/TCM-aided SM 292–3 transmit-diversity-enhanced SM 293–4

633

spatial modulation-OFDM (SM-OFDM) scheme 144, 149, 152–4 spectral efficiency (SE) 159–61 spectrally localized OFDM 101 adaptive symbol transitioned OFDM 102–3 OFDM with alignment signals 107–12 partial transmit sequenced OFDM 105–7 precoded OFDM 103–5 spectrum decision 485 spectrum efficiency analysis of massive MIMO 201 imperfect CSI 203–4 perfect CSI 201–3 spectrum map in cognitive radio networks by statistical inference and learning 537–8 spectrum mobility 486 spectrum sensing 485 spectrum sensing data falsification (SSDF) attack 570 spectrum-sensing techniques, traditional narrowband spectrum sensing 486 limitations 488 methodologies 487–8 wideband spectrum sensing 488 limitations 492 methodologies 488–91 spectrum sharing 486 sphere decoding detection 207 stacking auto-encoders (SAE) 524 standardized channel models 359 COST channel models (259 and 273) 359 IMT-advanced channel models from ITU 362 multidimensional parametric channel model 360 3GPP spatial channel model 360–1 WINNER channel model 361–2

634

Flexible and cognitive radio access technologies for 5G and beyond

state-of-the-art OFDM-IM solutions 180 coordinate interleaved OFDM-IM 183–4 dual-mode OFDM 182–3 generalized OFDM-IM 181–2 interleaved OFDM-IM 181 stochastic gradient decent (SGD) method 518 strong secrecy 551 subcarrier activation ratio 189 communication link, securing 190–1 Doppler spread, avoiding 189–90 robustness against hardware imperfection 190 subcarrier IM OFDM (SIM-OFDM) 176 subcarrier mapping scheme 70, 185 avoiding deep fading 188–9 equal bit protection 185–7 robustness against asynchronous transmission 187–8 subcarrier spacing (SCS) options 68 subcarrier spacing ratio 76–7 sub-connected hybrid beamforming 257–60 sub-Nyquist-based approaches 490 compressive sensing based (CSB) wideband spectrum sensing 491 multichannel sub-Nyquist (MCSN) wideband spectrum sensing 491 successive interference cancellation (SIC) 56, 295, 420, 463 surface plasmon polariton (SPP) waves 384 symbol error probability (SEP) 328 symbol timing error 43–5 synchronization 390–1 tapped delay line model 351–2 terahertz (THz) communication 11, 343–4, 379, 454 achieved data rates 391 application scenarios 381

entertainment technologies and augmented reality 383 fronthaul and backhaul links 381–2 heterogeneous networks 383 nano devices 382–3 channel and noise modeling 386 channel 386–7 molecular absorption noise and loss 387–8 future directions 398 physical layer 388 channel codes 389 medium access control 390 MIMO systems 389–90 modulation schemes 388–9 synchronization 390–1 transceivers design in terahertz band 384 amplifiers 385–6 wireless propagation channel for 392 measurement results 394–7 measurement setup 392–4 terahertz (THz) massive MIMO 243 terahertz band, transceivers design in 384 amplifiers 385–6 terrestrial base station (TBS) 451 3rd Generation Partnership Project (3GPP) enhanced 3GPP channel models 363 reference documents for 16–17 3GPP spatial channel model 360–1 time-dispersive multipath channel 36–9 time division duplex (TDD) mode 199 time division duplex (TDD) systems 115, 550 time division multiple access (TDMA) 50, 419 time domain 71–2 time-domain OFDM numerology multiplexing 120–1 time-spatial propagation (TSP) model 362

Index transceivers design in terahertz band 384 amplifiers 385–6 transmission points (TPs) 437 transmission time interval (TTI) 20, 440 transmit-diversity-enhanced spatial modulation 293–4 transmit/receive beamforming 250–1 two-ray propagation with RISs 320–3 ultra-dense networks (UDNs) 448 ultrahigh carrier frequencies and ultrawide bandwidths, rendering channels with 343–4 ultra-massive antenna settings, expressing channels in with beamforming 344–5 ultra-massive MIMO (umMIMO) technology 344 ultra-reliable low latency communication (URLLC) 8, 14, 29, 62, 67, 92, 122, 132, 165, 175, 444, 470 physical layer security (PLS) in 568 representing channels in 348 unidirectional orthogonality 91 uniform linear array (ULA) 206 uniform planar array (UPA) 206 unipolar OFDM (U-OFDM) 417 unique word (UW)-DFT-s-OFDM 61–2 universal filtered multicarrier (UFMC) waveform 57–8, 85, 92, 168 unmanned aerial vehicle (UAV), PLS in 569–70 utilized bandwidth (UBW) 89 variable gain amplifiers (VGAs) 254 variable PPM (VPPM) 415–16 vehicle-to-everything (V2X) communication 189 vehicle-to-infrastructure (V2I) 427 vehicle-to-vehicle (V2V) communications 345, 427

635

vehicular technology 427 Vertical Bell Labs Layered Space-Time (V-BLAST) 159 vertical federated learning (VFL) 532 virtual channel representation model 352–3 visible light communication (VLC) 11, 300, 403 applications of 422–9 channel modeling 409 channel parameters 413 indoor light propagation 409–11 line-of-sight (LOS) and non-line-of-sight (NLOS) channel models 411–13 indoor VLC systems 423 data centers 425–6 hospitals 424 industries 424–5 secure communication 426 integrated VLC systems 420 5G networks, integration of VLC in 421–2 infrared (IR) and VLC, integration of 421 power line communication (PLC) and VLC, integration of 421 radio frequency (RF) and VLC, integration of 421 medium access control (MAC) 419–20 optical modulation schemes 413 carrierless modulation schemes 414–15 multi-carrier modulation schemes 416–18 multicolor modulation schemes 418–19 single-carrier modulation schemes 415–16 outdoor 427–8 standardization activities 406 system design 406–20 underground 428–9 underwater 428

636

Flexible and cognitive radio access technologies for 5G and beyond

waveform coexistence for multiple accessing 473 OFDM with multi-numerology 476–7 OFDM with OFDM-IM 474–6 wideband and narrowband signals 473–4 waveform design 3 application requirements 8 cellular communications use cases 8–10 Wi-Fi communications standards 10–11 5G new radio standardization 15 bandwidth part issues 19–20 comparison for building blocks of 5G NR and LTE 21–3 numerology structures 17–19 slot structures 20–1 3GPP, reference documents for 16–17 frame structure 5 lattice structure 5 pulse shape 5 radio access technologies (RATs) 11 limitations and challenges for 11–12 performance indicators for waveform design 12–13 waveform design guidelines for 14–15 relationships of channel and RF impairments with a waveform 6–8 waveform multiplexing approaches for beyond 5G RATs 120 FDM of OFDM numerologies against hybrid waveforms 122–6 time-domain OFDM numerology multiplexing 120–1 wavelength division multiple access (WDMA) 419 wavelength transform based (WTB) spectrum sensing 489

Weichselberger channel model 349–51 wideband massive MIMO, physical layer signal processing in 242–3 wideband spectrum sensing 488 limitations 492 methodologies 488 Nyquist-based approaches 489–90 sub-Nyquist-based approaches 490–1 wide subcarrier numerology (WSN) 70–2, 82–5 windowed-OFDM (W-OFDM) 102 windowed-orthogonal frequency division multiplexing (W-OFDM) 52–3 WINNER channel model 361–2 WINNER II model 361 wireless channel 6 extraction of secret sequences from concepts, merits, and demerits 563 examples in time, frequency, and space 564–5 learned lessons 565 wireless communications, federated learning through 533–4 wireless fidelity (Wi-Fi) communications standards 10–11 wireless jamming attacks 576–7 adaptive jammer 578–9 constant jammer 577 intelligent jammer 579 intermittent jammer 577–8 reactive jammer 578 wireless networks coordination for 448 application and user requirements 454–5 communication technologies and standards 452–3 network architecture 448–51 smart radio environment 451–2 federated learning over 534–6

Index wireless propagation channel for terahertz band 392 measurement methodology 393–4 measurement results 394–7 measurement setup 392–4

zero-forcing (ZF) 255 signal detection 200–4 zero-padding (ZP) 36, 45 zero-tail (ZT)-DFT-s-OFDM 60–1 Zwick model 357–8

637