Liu / Qin / Ding Non-Orthogonal Multiple Access for Massive Connectivity

1. Auflage 2019
ISBN: 978-3-030-30975-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 107 Seiten

Reihe: SpringerBriefs in Computer Science

This book discusses non-orthogonal multiple access (NOMA) and the various issues in NOMA networks, including capability, sustainability, and security. This book starts from the basics and key techniques of NOMA. Subsequently, the authors discuss three critical issues in NOMA networks, including compatibility, sustainability, and security. Particularly, the authors first demonstrate the applications of NOMA in different networks including MIMO-NOMA, NOMA in heterogeneous networks, and NOMA in cognitive radio networks to show the compatibility of NOMA with various networks. Then the wireless powered NOMA networks are presented to address the sustainability issues in NOMA networks to extend the network reliability and lifetime. The security enhanced NOMA networks are discussed for single antenna case and multiple antenna case, respectively. Finally, the most recent developments on artificial intelligence (AI) enabled NOMA networks are discussed and the research challenges on NOMA to support massive number of devices are identified. 

Yuanwei Liu received the B.S. and M.S. degrees from the Beijing University of Posts and Telecommunications in 2011 and 2014, respectively, and the Ph.D. degree in electrical engineering from the Queen Mary University of London, U.K., in 2016. He was with the Department of Informatics, King's College London, from 2016 to 2017, where he was a Post-Doctoral Research Fellow. He has been a Lecturer (Assistant Professor) with the School of Electronic Engineering and Computer Science, Queen Mary University of London, since 2017. His research interests include 5G wireless networks, Internet of Things, machine learning, stochastic geometry, and matching theory. He received the Exemplary Reviewer Certificate of the IEEE Wireless Communication Letters in 2015 and the IEEE Transactions on Communications and the IEEE Transactions on Wireless Communications in 2016 and 2017. He has served as a TPC Member for many IEEE conferences, such as GLOBECOM and ICC. He currently serves as an Editor of the IEEE Transactions on Communications, the IEEE Communications Letters and the IEEE Access. He is also a guest editor for IEEE JSTSP special issue on 'Signal Processing Advances for Non-Orthogonal Multiple Access in Next Generation Wireless Networks'.   Zhijin Qin received the bachelor's degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 2012 and the Ph.D. degree from Queen Mary University of London (QMUL), London, U.K., in 2016. She joined QMUL as a Lecturer (Assistant Professor) in the School of Electronic Engineering and Computer Science since August 2018. Before that, she was with Lancaster University and Imperial College London as a Lecturer and Research Associate, respectively. Her research interests include low-power wide area networks for Internet of Things, compressive sensing and machine learning in wireless communications, and non-orthogonal multiple access. She currently serves as an Editor of the IEEE Communications Letter. She has served as a TPC member for various IEEE conferences. She received the Best Paper Award from Wireless Technology Symposium 2012, IEEE Global Communications Conference (GLOBECOM) 2017, and IEEE Signal Processing Society Young Author Best Paper Award 2018.   Zhiguo Ding received the B.Eng. degree in electrical engineering from the Beijing University of Posts and Telecommunications in 2000 and the Ph.D. degree in electrical engineering from Imperial College London in 2005. From 2005 to 2018, he was with Queen's University Belfast, Imperial College, Newcastle University, and Lancaster University. Since 2018, he has been with the University of Manchester as a Professor in communications. From 2012 to 2018, he has also been an Academic Visitor with Princeton University. His research interests are 5G networks, game theory, cooperative and energy harvesting networks, and statistical signal processing. He received the Best Paper Award in IET ICWMC-2009 and IEEE WCSP-2014, the EU Marie Curie Fellowship 2012-2014, the Top IEEE TVT Editor 2017, the IEEE Heinrich Hertz Award 2018, and the IEEE Jack Neubauer Memorial Award 2018. He serves as an Editor for the IEEE TRANSACTIONS ON COMMUNICATIONS, the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, and the Journal of Wireless Communications and Mobile Computing, and he was an Editor of the IEEE WIRELESS COMMUNICATION LETTERS and the IEEE COMMUNICATION LETTERS from 2013 to 2016.

Autoren/Hrsg.

Liu, Yuanwei

Qin, Zhijin

Ding, Zhiguo

Inhaltsverzeichnis

1;Foreword;7
2;Preface;8
2.1;Acknowledgement;8
3;Contents;9
4;Acronyms;10
5;Part I Background;12
5.1;1 Introduction;13
5.1.1;1.1 Background;13
5.1.2;1.2 Standardization of NOMA;14
5.1.3;References;15
5.2;2 What Is NOMA?;17
5.2.1;2.1 NOMA Basis;17
5.2.1.1;2.1.1 Investigating NOMA from an Information Theoretic Perspective;17
5.2.1.2;2.1.2 Downlink NOMA Transmission;18
5.2.1.3;2.1.3 Uplink NOMA Transmission;20
5.2.2;References;21
6;Part II NOMA in Future Wireless Networks;23
6.1;3 Compatibility in NOMA;24
6.1.1;3.1 NOMA in Heterogeneous Networks;24
6.1.1.1;3.1.1 Network Model;25
6.1.1.1.1;3.1.1.1 Network Description;25
6.1.1.1.2;3.1.1.2 NOMA and Massive MIMO-Based User Association;26
6.1.1.1.3;3.1.1.3 Channel Model;27
6.1.1.2;3.1.2 Coverage Probability of Non-orthogonal Multiple-Access-Based Small Cells;29
6.1.1.2.1;3.1.2.1 User Association Probability and Distance Distributions;29
6.1.1.2.2;3.1.2.2 Laplace Transform of Interferences;31
6.1.1.2.3;3.1.2.3 Coverage Probability;32
6.1.1.3;3.1.3 Spectrum Efficiency;34
6.1.1.3.1;3.1.3.1 Ergodic Rate of NOMA Enhanced Small Cells;34
6.1.1.3.2;3.1.3.2 Ergodic Rate of Macro Cells;36
6.1.1.3.3;3.1.3.3 Spectrum Efficiency of the Proposed Hybrid Hetnets;37
6.1.1.4;3.1.4 Energy Efficiency;37
6.1.1.4.1;3.1.4.1 Power Consumption Model;38
6.1.1.4.2;3.1.4.2 Energy Efficiency of NOMA Enhanced Small Cells and Macro Cells;38
6.1.1.4.3;3.1.4.3 Energy Efficiency of the Proposed Hybrid Hetnets;39
6.1.1.5;3.1.5 Numerical Results;39
6.1.1.5.1;3.1.5.1 User Association Probability and Coverage Probability;39
6.1.1.5.2;3.1.5.2 Spectrum Efficiency;42
6.1.1.5.3;3.1.5.3 Energy Efficiency;43
6.1.2;3.2 NOMA in Cognitive Radio Networks;44
6.1.3;3.3 NOMA with MIMO;45
6.1.3.1;3.3.1 Cluster-Based MIMO-NOMA;46
6.1.3.2;3.3.2 Beamformer-Based MIMO-NOMA;47
6.1.3.3;3.3.3 Massive-MIMO-NOMA;49
6.1.3.4;3.3.4 Cognitive Radio Inspired Power Control;49
6.1.3.5;3.3.5 NOMA-Based Device-to-Device Communications;50
6.1.4;3.4 Summary;51
6.1.5;References;51
6.2;4 Sustainability of NOMA;54
6.2.1;4.1 Cooperative NOMA Networks;54
6.2.1.1;4.1.1 Cooperative NOMA;54
6.2.1.2;4.1.2 NOMA in Cooperative Transmission-Based Networks;56
6.2.1.2.1;4.1.2.1 Relay-Aided NOMA Transmission;56
6.2.1.2.2;4.1.2.2 Multi-Cell NOMA with Coordinated Multipoint Transmission;56
6.2.2;4.2 Wireless Powered NOMA;57
6.2.2.1;4.2.1 Network Model;58
6.2.2.1.1;4.2.1.1 Phase 1: Direct Transmission;59
6.2.2.1.2;4.2.1.2 Phase 2: Cooperative Transmission;61
6.2.2.2;4.2.2 Non-orthogonal Multiple Access with User Selection;62
6.2.2.2.1;4.2.2.1 RNRF Selection Scheme;62
6.2.2.2.2;4.2.2.2 NNNF Selection Scheme;65
6.2.2.2.3;4.2.2.3 NNFF Selection Scheme;67
6.2.2.3;4.2.3 Numerical Results;69
6.2.2.3.1;4.2.3.1 Outage Probability of the Near Users;69
6.2.2.3.2;4.2.3.2 Outage Probability of the Far Users;71
6.2.3;4.3 Summary;73
6.2.4;References;73
6.3;5 Security in NOMA;75
6.3.1;5.1 Secure NOMA in Random Wireless Networks;75
6.3.1.1;5.1.1 System Model;76
6.3.1.2;5.1.2 New Channel Statistics;78
6.3.1.3;5.1.3 Secrecy Outage Probability;79
6.3.1.4;5.1.4 Secrecy Diversity Order Analysis;80
6.3.2;5.2 Enhancing Security with the Aid of Artificial Noise;83
6.3.2.1;5.2.1 New Channel Statistics;85
6.3.2.2;5.2.2 Secrecy Outage Probability;86
6.3.2.3;5.2.3 Large Antenna Array Analysis;88
6.3.2.4;5.2.4 Numerical Results;91
6.3.2.5;5.2.5 Secrecy Outage Probability with Channel Ordering;91
6.3.2.6;5.2.6 Secrecy Outage Probability with Artificial Noise;93
6.3.3;5.3 Summary;95
6.3.4;References;95
6.4;6 Artificial Intelligence (AI) Enabled NOMA;96
6.4.1;6.1 AI for Adaptive NOMA;96
6.4.1.1;6.1.1 Unified NOMA;96
6.4.2;6.2 NOMA in UAV Networks;98
6.4.3;6.3 Summary;101
6.4.4;References;101
7;Part III Challenges and Conclusions;102
7.1;7 Challenges and Conclusions;103
7.1.1;7.1 Research Challenges;103
7.1.2;7.2 Conclusions;104
8;Index;105