Massive MIMO for Future Wireless Communication Systems

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.

• About the Editors xviiList of Contributors xixPreface xxvAcknowledgments xxxi1 Fundamentals of Wireless Communications: Massive MIMO Essentials for 6G and Beyond 1Webert Montlouis and Agbotiname Lucky Imoize1.1 Introduction 11.2 Digital and Analog Sources 41.3 Deterministic and Random Waveforms 51.4 Propagation of Electromagnetic Waves 61.5 Information Measures 71.6 Channel and Information 81.7 Modulation and Demodulation 91.8 Massive MIMO 111.9 Security and Privacy of Wireless Systems 151.10 Conclusion 18References 182 Security and Privacy of Future Wireless Communication Systems 23Abdulwaheed Musa, Abdullateef Ola Adebayo, Peace Oluwasijibomi Balogun, and Segun Jacob2.1 Introduction 232.2 Overview of the Current State of Massive MIMO Wireless Communication Systems 252.3 Related Reviews 302.4 Security and Privacy Challenges of Future Wireless Communication Systems 352.5 Physical Layer Security and Technologies for Massive MIMO 392.6 Privacy-Enhancing Technologies 442.7 Emerging Technologies 462.8 Potential Threats and Challenges of Future Wireless Communication Systems 472.9 Open Research Issues and the Future of Security and Privacy 482.10 Limitations of the Study 502.11 Lessons Learned 502.12 Conclusion and Future Scope 51References 523 Applications of Massive MIMO in Wireless Communication Systems 59Abdulwaheed Musa, Peace Oluwasijibomi Balogun, and Abdullateef Ola Adebayo3.1 Introduction 593.2 Evolution from MIMO to Massive MIMO and Role in 5G 613.3 Related Works 643.4 Fundamental Limits 673.5 Benefits of Massive MIMO for 6G and Beyond 743.6 Design and Implementation Challenges for Massive MIMO in 6G 763.7 Enabling Technologies for Massive MIMO in 6G 773.8 Applications of Massive MIMO in Wireless Communication Systems 793.9 Recent Advances and Future Outlook for Massive MIMO in 6G 843.10 Limitations of the Study 873.11 Lessons Learned 883.12 Conclusion and Future Scope 89References 904 Cell-Free Massive MIMO Technology and Applications in 6G 95Joumana Kassam, Daniel Castanheira, Adão Silva, Rui Dinis, and Atílio Gameiro4.1 Introduction 954.2 Conventional Cell-Free mMIMO Systems 994.3 Scalable User-Centric Cell-Free mMIMO Systems 1074.4 Radio Stripes Cell-Free Systems 1134.5 Conclusions and Future Work 116Acknowledgment 117References 1175 Localization in Massive MIMO Networks: From Far-Field to Near-Field 123Parisa Ramezani, Özlem Tuğfe Demir, and Emil Björnson5.1 Introduction 1235.2 Far-Field DoA Estimation 1255.3 Near-Field DoA and Range Estimation 1355.4 Conclusions 146References 1486 Energy-Efficient Uplink Transmission in RIS-Aided M-MIMO IoT Systems 151David William Marques Guerra, José Carlos Marinello, Ekram Hossain, and Taufik Abrão6.1 Introduction 1516.2 Related Works 1576.3 System Model for RIS-Aided M-MIMO 1606.4 Definitions on Riemannian Manifolds 1666.5 Energy-Efficient Uplink Transmission in RIS M-MIMO IoT Systems 1686.6 Conclusion and Research Directions 183References 1847 Energy Efficiency in RIS-Aided Massive MIMO and XL-MIMO Communication Systems 189Wilson Souza Junior, João Henrique Inacio de Souza, José Carlos Marinello, and Taufik Abrão7.1 Introduction 1897.2 General System Model for RIS-Aided M-MIMO 2017.3 Energy Efficiency in RIS-Aided M-MIMO 2127.4 Energy Efficiency in Demand-Adaptive XL-MIMO Systems 2327.5 Conclusions and Perspective 244References 2478 NOMA-Aided Massive MIMO for Next-Generation Networks 251Vinoth Babu Kumaravelu, Helen Sheeba J., Dipinkrishnan R., Arthi Murugadass, Poongundran Selvaprabhu, Rajeshkumar V., Vetriveeran Rajamani, Anand Sreekantan Thampy, Agbotiname Lucky Imoize, and Webert Montlouis8.1 Introduction 2518.2 Related Work 2548.3 System Model 2628.4 Simulations and Discussions 2678.5 Conclusion 275References 2759 Efficient Hybrid Precoding for Millimeter-Wave Massive MIMO-NOMA Systems: A Low-Complexity Approach 281Samarendra Nath Sur, Arun Kumar Singh, Agbotiname Lucky Imoize, Joseph Bamidele Awotunde, Debdatta Kandar, and Vinoth Babu Kumaravelu9.1 Introduction 2819.2 Overview of Basic Precoding Scheme 2849.3 Related Work 2899.4 Mathematical Model 2949.5 Results 2969.6 Challenges of Hybrid Precoding and Future Research Scope 2999.7 Conclusion 300References 30110 Intelligent Reflecting Surfaces and Next-Generation Wireless Systems 309Yashuai Cao, Hetong Wang, Tiejun Lv, and Wei Ni10.1 Introduction 30910.2 Related Work 31310.3 Intelligent Reflecting Surfaces Hardware and Architectures 31410.4 Intelligent Reflecting Surfaces and the Path Loss Model 31710.5 IRS-Empowered Slot Scheduling and Cost-Efficient Reflection Optimization 32010.6 Two-Timescale Reflection Pattern Design 32810.7 Results and Discussions 33610.8 Conclusion 34010.9 Future Work 341References 34111 ABER Performance Evaluation of RIS-Aided Millimeter Wave Massive MIMO System Under 3GPP 5G Channels 347Vishnu 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 Murugadass11.1 Introduction 34711.2 Related Work 35311.3 System Model 35611.4 Channel Model 35811.5 Simulations and Discussions 36011.6 Conclusions 365References 36612 Massive MIMO for Non-terrestrial Wireless Communication Systems 371Unwana Macaulay Ekpe, Agbotiname Lucky Imoize, and Webert Montlouis12.1 Introduction 37112.2 The Role of Satellites in Future Non-terrestrial Networks 37612.3 Signal Processing for Non-terrestrial Network-Based Massive MIMO Systems 38112.4 Single-Cell Massive MIMO Linear Precoding Techniques 38512.5 Multi-cell Massive MIMO Linear Precoding Techniques 38812.6 Security Considerations in a Non-terrestrial Network 39212.7 Standards and Interoperability Requirements in a Non-terrestrial Network 39512.8 Conclusion and Recommendations 399References 40013 Artificial Intelligence and Machine Learning for Channel Estimation in Massive MIMO Wireless Communication Systems 403Thiruvengadam Sundarrajan Jayaraman, Velmurugan Periyakarupan Gurusamy Sivabalan, Vinoth Babu Kumaravelu, Agbotiname Lucky Imoize, and Helen Sheeba John Kennedy13.1 Introduction 40313.2 Related Work 40613.3 Channel Estimation Using LS and MMSE Estimators 41013.5 FL in Cell-Free Massive MIMO: An Overview 42913.6 Protection of Privacy of User Data in Cell-Free Massive MIMO Using FL 43113.7 Results and Discussions 43213.8 Conclusions 437References 437Index 441