About the Book
Authoritative resource discussing the development of advanced massive multiple input multiple output (MIMO) techniques and algorithms for application in 6G
Massive MIMO for Future Wireless Communication Systems analyzes applications and technology trends for massive multiple input multiple output (MIMO) in 6G and beyond, presenting a unified theoretical framework for analyzing the fundamental limits of massive MIMO that considers several practical constraints. In addition, this book develops advanced signal-processing algorithms to enable massive MIMO applications in realistic environments.
The book looks closer at applying techniques to massive MIMO in order to meet practical network constraints in 6G networks, such as interference, pathloss, delay, and traffic outage, and provides new insights into real-world deployment scenarios, applications, management, and associated benefits of robust, provably secure, and efficient security and privacy schemes for massive MIMO wireless communication networks.
To aid in reader comprehension, this book includes a glossary of terms, resources for further reading via a detailed bibliography, and useful figures and summary tables throughout.
With contributions from industry experts and researchers across the world and edited by two leaders in the field, Massive MIMO for Future Wireless Communication Systems includes information on:
Signal processing algorithms for cell-free massive MIMO systems and advanced mathematical tools to analyze multiuser dynamics in wireless channels
Bit error rate (BER) performance comparisons of different detectors in conventional cell-free massive MIMO systems
Enhancement of massive MIMO using deep learning-based channel estimation and cell-free massive MIMO for wireless federated learning
Low-complexity, self-organizing, and energy-efficient massive MIMO architectures, including the prospects and challenges of Terahertz MIMO systems
Massive MIMO for Future Wireless Communication Systems is an essential resource on the subject for industry and academic researchers, advanced students, scientists, and engineers in the fields of MIMO, antennas, sensing and channel measurements, and modeling technologies.
Table of Contents:
About the Editors xvii
List of Contributors xix
Preface xxv
Acknowledgments xxxi
1 Fundamentals of Wireless Communications: Massive MIMO Essentials for 6G and Beyond 1
Webert Montlouis and Agbotiname Lucky Imoize
1.1 Introduction 1
1.2 Digital and Analog Sources 4
1.3 Deterministic and Random Waveforms 5
1.4 Propagation of Electromagnetic Waves 6
1.5 Information Measures 7
1.6 Channel and Information 8
1.7 Modulation and Demodulation 9
1.8 Massive MIMO 11
1.9 Security and Privacy of Wireless Systems 15
1.10 Conclusion 18
References 18
2 Security and Privacy of Future Wireless Communication Systems 23
Abdulwaheed Musa, Abdullateef Ola Adebayo, Peace Oluwasijibomi Balogun, and Segun Jacob
2.1 Introduction 23
2.2 Overview of the Current State of Massive MIMO Wireless Communication Systems 25
2.3 Related Reviews 30
2.4 Security and Privacy Challenges of Future Wireless Communication Systems 35
2.5 Physical Layer Security and Technologies for Massive MIMO 39
2.6 Privacy-Enhancing Technologies 44
2.7 Emerging Technologies 46
2.8 Potential Threats and Challenges of Future Wireless Communication Systems 47
2.9 Open Research Issues and the Future of Security and Privacy 48
2.10 Limitations of the Study 50
2.11 Lessons Learned 50
2.12 Conclusion and Future Scope 51
References 52
3 Applications of Massive MIMO in Wireless Communication Systems 59
Abdulwaheed Musa, Peace Oluwasijibomi Balogun, and Abdullateef Ola Adebayo
3.1 Introduction 59
3.2 Evolution from MIMO to Massive MIMO and Role in 5G 61
3.3 Related Works 64
3.4 Fundamental Limits 67
3.5 Benefits of Massive MIMO for 6G and Beyond 74
3.6 Design and Implementation Challenges for Massive MIMO in 6G 76
3.7 Enabling Technologies for Massive MIMO in 6G 77
3.8 Applications of Massive MIMO in Wireless Communication Systems 79
3.9 Recent Advances and Future Outlook for Massive MIMO in 6G 84
3.10 Limitations of the Study 87
3.11 Lessons Learned 88
3.12 Conclusion and Future Scope 89
References 90
4 Cell-Free Massive MIMO Technology and Applications in 6G 95
Joumana Kassam, Daniel Castanheira, Adão Silva, Rui Dinis, and Atílio Gameiro
4.1 Introduction 95
4.2 Conventional Cell-Free mMIMO Systems 99
4.3 Scalable User-Centric Cell-Free mMIMO Systems 107
4.4 Radio Stripes Cell-Free Systems 113
4.5 Conclusions and Future Work 116
Acknowledgment 117
References 117
5 Localization in Massive MIMO Networks: From Far-Field to Near-Field 123
Parisa Ramezani, Özlem Tuğfe Demir, and Emil Björnson
5.1 Introduction 123
5.2 Far-Field DoA Estimation 125
5.3 Near-Field DoA and Range Estimation 135
5.4 Conclusions 146
References 148
6 Energy-Efficient Uplink Transmission in RIS-Aided M-MIMO IoT Systems 151
David William Marques Guerra, José Carlos Marinello, Ekram Hossain, and Taufik Abrão
6.1 Introduction 151
6.2 Related Works 157
6.3 System Model for RIS-Aided M-MIMO 160
6.4 Definitions on Riemannian Manifolds 166
6.5 Energy-Efficient Uplink Transmission in RIS M-MIMO IoT Systems 168
6.6 Conclusion and Research Directions 183
References 184
7 Energy Efficiency in RIS-Aided Massive MIMO and XL-MIMO Communication Systems 189
Wilson Souza Junior, João Henrique Inacio de Souza, José Carlos Marinello, and Taufik Abrão
7.1 Introduction 189
7.2 General System Model for RIS-Aided M-MIMO 201
7.3 Energy Efficiency in RIS-Aided M-MIMO 212
7.4 Energy Efficiency in Demand-Adaptive XL-MIMO Systems 232
7.5 Conclusions and Perspective 244
References 247
8 NOMA-Aided Massive MIMO for Next-Generation Networks 251
Vinoth Babu Kumaravelu, Helen Sheeba J., Dipinkrishnan R., Arthi Murugadass, Poongundran Selvaprabhu, Rajeshkumar V., Vetriveeran Rajamani, Anand Sreekantan Thampy, Agbotiname Lucky Imoize, and Webert Montlouis
8.1 Introduction 251
8.2 Related Work 254
8.3 System Model 262
8.4 Simulations and Discussions 267
8.5 Conclusion 275
References 275
9 Efficient Hybrid Precoding for Millimeter-Wave Massive MIMO-NOMA Systems: A Low-Complexity Approach 281
Samarendra Nath Sur, Arun Kumar Singh, Agbotiname Lucky Imoize, Joseph Bamidele Awotunde, Debdatta Kandar, and Vinoth Babu Kumaravelu
9.1 Introduction 281
9.2 Overview of Basic Precoding Scheme 284
9.3 Related Work 289
9.4 Mathematical Model 294
9.5 Results 296
9.6 Challenges of Hybrid Precoding and Future Research Scope 299
9.7 Conclusion 300
References 301
10 Intelligent Reflecting Surfaces and Next-Generation Wireless Systems 309
Yashuai Cao, Hetong Wang, Tiejun Lv, and Wei Ni
10.1 Introduction 309
10.2 Related Work 313
10.3 Intelligent Reflecting Surfaces Hardware and Architectures 314
10.4 Intelligent Reflecting Surfaces and the Path Loss Model 317
10.5 IRS-Empowered Slot Scheduling and Cost-Efficient Reflection Optimization 320
10.6 Two-Timescale Reflection Pattern Design 328
10.7 Results and Discussions 336
10.8 Conclusion 340
10.9 Future Work 341
References 341
11 ABER Performance Evaluation of RIS-Aided Millimeter Wave Massive MIMO System Under 3GPP 5G Channels 347
Vishnu Vardhan Gudla, Vinoth Babu Kumaravelu, Anjana B. S., Poongundran Selvaprabhu, Nivetha Baskar, Helen Sheeba John Kennedy, Samarendra Nath Sur, Webert Montlouis, Agbotiname Lucky Imoize, and Arthi Murugadass
11.1 Introduction 347
11.2 Related Work 353
11.3 System Model 356
11.4 Channel Model 358
11.5 Simulations and Discussions 360
11.6 Conclusions 365
References 366
12 Massive MIMO for Non-terrestrial Wireless Communication Systems 371
Unwana Macaulay Ekpe, Agbotiname Lucky Imoize, and Webert Montlouis
12.1 Introduction 371
12.2 The Role of Satellites in Future Non-terrestrial Networks 376
12.3 Signal Processing for Non-terrestrial Network-Based Massive MIMO Systems 381
12.4 Single-Cell Massive MIMO Linear Precoding Techniques 385
12.5 Multi-cell Massive MIMO Linear Precoding Techniques 388
12.6 Security Considerations in a Non-terrestrial Network 392
12.7 Standards and Interoperability Requirements in a Non-terrestrial Network 395
12.8 Conclusion and Recommendations 399
References 400
13 Artificial Intelligence and Machine Learning for Channel Estimation in Massive MIMO Wireless Communication Systems 403
Thiruvengadam Sundarrajan Jayaraman, Velmurugan Periyakarupan Gurusamy Sivabalan, Vinoth Babu Kumaravelu, Agbotiname Lucky Imoize, and Helen Sheeba John Kennedy
13.1 Introduction 403
13.2 Related Work 406
13.3 Channel Estimation Using LS and MMSE Estimators 410
13.5 FL in Cell-Free Massive MIMO: An Overview 429
13.6 Protection of Privacy of User Data in Cell-Free Massive MIMO Using FL 431
13.7 Results and Discussions 432
13.8 Conclusions 437
References 437
Index 441
About the Author :
Webert Montlouis (Fellow, IEEE) received the PhD degree in electrical and computer engineering from Northeastern University, Boston, MA, USA. He is currently with Johns Hopkins University, Baltimore, MD. He has served as Chief Scientist at the Applied Physics Laboratory (APL) and faculty in the Electrical and Computer Engineering department. He has been the chair of the IEEE Massive MIMO standard development working group. He is also the co-Chair of the Massive MIMO working group. Dr, Montlouis served as general co-Chair of the first IEEE Massive MIMO workshop and served as session chair for many IEEE conferences. His research interests are in the areas Multi-Channel System Architecture, Sensing, Next Generation Radar Systems, Wireless Communications 5G and Beyond, Quantum Information Science, Digital signal Processing and Biomedical Signal Processing. He is a Fellow of the IEEE and a member of the IEEE Signal Processing and Communications Societies.
Agbotiname Lucky Imoize is a Lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. Previously, he was a Research Scholar at the Ruhr University Bochum, Germany. He is a Fulbright fellow and a Senior Member of the IEEE.
