Reconfigurable Intelligent Surfaces for 6G and Beyond Wireless Networks E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 928

Veröffentlichungsjahr: 2025

Beschreibung

New insights into trends, deployments, applications, and associated benefits of reconfigurable intelligent surfaces (RIS) in emerging wireless communication systems

Reconfigurable Intelligent Surfaces for 6G and Beyond Wireless Networks analyzes the design and applications of RIS in 6G and beyond, such as aiding efficient wireless signal transmission from the transmitter to the receiver while considering several practical constraints. In addition, the book offers advanced signal-processing algorithms to enable RIS applications in realistic environments and leverages advanced mathematical tools and machine learning algorithms to analyze RIS dynamics in evolving wireless networks.

Written in an easy-to-understand format, this book addresses the need to design energy- and spectral-efficient RIS models to address several network issues, including interference, pathloss, delay, traffic outage, etc. It also discusses critical security and privacy issues affecting all stakeholders in the wireless ecosystem, providing practical deep learning-based solutions to address these problems appropriately.

This book also addresses critical concepts, design principles, applications, and issues with RIS, shedding light on the recent progress and advancement in RIS-assisted wireless networks for 6G and beyond.

With contributions from experts and researchers from across the globe, this invaluable resource includes information on:

• Emerging applications and potential use cases of reconfigurable intelligent surfaces in advanced wireless communication systems
• Channel modeling and propagation measurements in RIS-based wireless communication systems
• Energy and spectral efficiency and rate fairness for RIS-aided multiuser massive MIMO systems
• Performance optimization of multiple RIS-assisted multiuser MIMO communication systems
• Analytical phase-shift and amplitude element optimization for energy-efficient active RIS-aided massive MIMO systems
• Physical layer security architecture and frameworks for RIS-aided wireless communication systems
• RIS deployment in terrestrial and non-terrestrial wireless communication systems
• Application of AI and ML techniques for intelligent power control in RIS-empowered wireless communication systems

Reconfigurable Intelligent Surfaces for 6G and Beyond Wireless Networks is an essential up-to-date reference on the subject for industry and academic researchers, scientists, and engineers in the fields of wireless communications, ICTs, MIMO, antennas, sensing, channel measurements, and modeling technologies, as well as engineers and professionals involved in RIS-assisted wireless networks.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Editors

About the Contributors

Preface

Acknowledgments

Introduction

1 Reconfigurable Intelligent Surfaces‐Assisted Wireless Communication Systems: Baseband Processing Perspective

1.1 Introduction

1.2 Related Work

1.3 Comprehensive Overview of RIS

1.4 Characterization of RIS

1.5 Results and Discussions

1.6 Conclusion and Future Directions

References

2 Emerging Applications and Potential Use Cases of Reconfigurable Intelligent Surfaces in Wireless Communication Systems

2.1 Introduction

2.2 Use Cases of RIS

2.3 RIS Applications

2.4 Challenges and Research Trends

2.5 Conclusions

References

3 Reconfigurable Intelligent Surfaces for 6G: A Comprehensive Overview and Electromagnetic Analysis

3.1 Introduction

3.2 Overview of RIS

3.3 EM Analysis of RIS

3.4 Impact of Mutual Coupling in RIS

3.5 Impact of Spatial Correlation in RIS

3.6 Results and Discussion

3.7 Conclusions

References

4 Spectral Efficiency and Rate Fairness for RIS‐Aided Multiuser Massive MIMO System

4.1 Introduction

4.2 Fundamental Concepts

4.3 Methodology

4.4 Numerical Results

4.5 Conclusions

References

Note

5 Performance Optimization of Multiple RIS‐Assisted Multiuser MIMO Communication Systems

5.1 Introduction

5.2 Related Work

5.3 N‐RIS‐assisted MU‐MIMO System Model

5.4 Blind RISs and Optimized Transmission

5.5 System Performance Results

5.6 Artificial Intelligence in RIS‐assisted MU‐MIMO Systems

5.7 Future Trends, Challenges, and Opportunities

5.8 Conclusion

References

Note

6 Analytical Phase Shift and Amplitude Element Optimization for Energy‐Efficient Active RIS‐Aided Massive MIMO Systems

6.1 Introduction

6.2 Related Works

6.3 General System Model for RIS‐aided M‐MIMO

6.4 Optimization Techniques

6.5 Problem Formulation

6.6 Proposed Solution

6.7 Numerical Results

6.8 Conclusions and Perspectives

References

Notes

7 Element Grouping in IRS‐Aided Wireless Communication System

7.1 Introduction

7.2 Element Grouping

7.3 Mathematical Model

7.4 Results

7.5 Conclusion and Future Scope

References

8 Reconfigurable Intelligent Surface‐Empowered Non‐orthogonal Multiple Access: Outage and ABER Analysis of Smart and Blind Transmissions

8.1 Introduction

8.2 Related Works

8.3 RIS‐AP‐NOMA

8.4 Analytical Model

8.5 Discussions on Simulations

8.6 Conclusions

References

9 Convergence of RIS with Emerging Wireless Technologies

9.1 Introduction

9.2 RIS‐Assisted Wireless Communication Systems

9.3 Theoretical Analysis of RIS‐Assisted Systems

9.4 Conclusions

References

10 A Survey on RIS for 6G–IoT Wireless Positioning and Localization

10.1 Introduction

10.2 Role of RIS‐Assisted IoT Networks in Wireless Positioning and Localization

10.3 Localization Principles and RIS‐Aided Localization Algorithms

10.4 State‐of‐the‐Art Research on Positioning and Localization with the Assistance of RIS in 6G–IoT

10.5 Potential Challenges of RIS‐Aided 6G–IoT for Wireless Positioning and Localization

10.6 Future Research Directions

10.7 Conclusions

References

11 Security and Privacy Issues in RIS‐Based Wireless Communication Systems

11.1 Introduction

11.2 Related Work

11.3 Ensuring Security and Privacy in 6G Applications Assisted by RIS

11.4 Various Threats and Attacks in RIS‐Supported Wireless Systems

11.5 Secure Physical Layer Networks for RIS‐Assisted System

11.6 Conclusions and Future Scope

References

12 AI and ML Techniques for Intelligent Power Control in RIS‐Empowered Wireless Communication Systems

12.1 Introduction

12.2 Related Work

12.3 DRL Algorithms for RIS‐Assisted Wireless Communication Systems

12.4 Proposed Method for Intelligent Power Control

12.5 Results and Analysis

12.6 Discussions

12.7 Limitations of the Survey

12.8 Critical Lessons Learned

12.9 Conclusion

12.10 Future Scope

References

13 An Overview of Channel Modeling and Propagation Measurements in IRS‐Based Wireless Communication Systems

13.1 Introduction

13.2 Related Work

13.3 IRS Technology and Its Fundamental Principles

13.4 Channel Modeling and Propagation Measurements for Communication Systems

13.5 IRS Channel Modeling

13.6 Limitations of the Survey

13.7 Critical Lessons Learned

13.8 Potential Challenges of Propagation Measurements and Channel Modeling

13.9 Conclusion and Future Scope

References

14 Deep Reinforcement Algorithms in RIS‐Empowered Wireless Communication Systems

14.1 Introduction

14.2 Related Works

14.3 What is Deep Reinforcement Learning?

14.4 Deep Reinforcement Learning Algorithms for RIS‐Empowered Wireless Communication Systems

14.5 Limitations and Key Takeaways

14.6 Conclusion

References

15 Examining Physical Layer Security for RIS‐Aided Wireless Communication Systems

15.1 Introduction

15.2 Related Works

15.3 Fundamentals of PLS, RIS, and IoT

15.4 RIS‐Aided PLS

15.5 Mathematical Analysis of PLS in an RIS‐Aided NOMA Network

15.6 Limitations and Key Takeaways

15.7 Conclusion

References

16 RIS‐Empowered Terrestrial and Non‐terrestrial Wireless Communication Systems

16.1 Introduction

16.2 Metamaterials and Metasurfaces

16.3 RIS‐Empowered Terrestrial Communication Systems

16.4 RIS‐Empowered Non‐Terrestrial Communication Systems

16.5 Lessons Learned and Future Research Direction

16.6 Conclusion

References

17 Energy Efficiency and Optimization of RIS‐Based Wireless Communication Systems

17.1 Introduction

17.2 Optimization for RIS‐Based Transmission

17.3 Cooperative RIS

17.4 Optimization for RIS‐Assisted Beamforming

17.5 RIS Partitioning

17.6 Simulation Results of Multiple RISs Systems

17.7 Discussion

17.8 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Mean and variance factors ,

Table 1.2 Simulation parameters.

Table 1.3 Impact of on the performance of the discrete phase shifter to a...

Chapter 4

Table 4.1 Related work that addresses current open issues in channel condit...

Table 4.2 The key summarized results and observations of this chapter, comp...

Table 4.3 System parameters and configurations of RIS‐aided M‐MIMO systems ...

Table 4.4 System parameters and configurations of RIS‐aided M‐MIMO systems ...

Table 4.5 System parameters and configurations of RIS‐aided M‐MIMO systems ...

Table 4.6 System parameters and configurations of RIS‐aided M‐MIMO systems ...

Chapter 5

Table 5.1 Related approaches for optimization of RIS‐aided systems using MI...

Table 5.2 Simulation parameters for the different RIS scenarios considered ...

Table 5.3 Simulation results obtained from the different multi‐RIS system c...

Table 5.4 Detection complexity.

Chapter 6

Table 6.1 Recent works addressing Resource Efficiency (EE and SE) and relat...

Table 6.2 List of notation and symbols for the RIS‐aided M‐MIMO scenario....

Table 6.3 List of simulation parameters.

Table 6.4 Summary of the contributions – key quantitative results and a bri...

Chapter 7

Table 7.1 Summary: IRS element grouping related works.

Chapter 8

Table 8.1 Parameters for numerical simulations.

Table 8.2 SNR requirement (in dB) of smart and blind RIS‐AP‐NOMA users for ...

Table 8.3 SNR requirement (in dB) of smart and blind RIS‐AP‐NOMA users for ...

Chapter 10

Table 10.1 Related works of RIS‐aided 6G–IoT using the four‐layered network...

Table 10.2 Related works of RIS for outdoor and far‐field localization.

Table 10.3 Related works of RIS for outdoor and far‐field localization.

Chapter 11

Table 11.1 Related works on security and privacy issues in RIS‐assisted wir...

Chapter 12

Table 12.1 Research efforts on AI and ML techniques.

Table 12.2 Limitation of related work.

Table 12.3 The simulation parameters.

Chapter 13

Table 13.1 Related works on channel modeling for wireless communication sys...

Table 13.2 Transmission over several networks.

Table 13.3 IRS literature.

Table 13.4 Comparison of NOMA and OMA techniques.

Table 13.5 Literature on NOMA networks performance.

Table 13.6 Performance evaluation of IRS‐assisted NOMA networks.

Chapter 14

Table 14.1 Energy efficiency works summary.

Table 14.2 Survey papers summary.

Table 14.3 Technique comparison in terms of performance in RIS.

Table 14.4 Detailed build and inner‐workings of models.

Chapter 15

Table 15.1 Summary of RIS‐aided wireless approaches.

Table 15.2 RIS compared to other modes of transmission techniques.

Chapter 16

Table 16.1 Comparison of control mechanisms for metasurfaces.

Table 16.2 Signal routing options for multilayered integrated RIS‐empowered...

Chapter 17

Table 17.1 Papers surveyed.

Table 17.2 Parameters in channel equation of cooperative RIS.

Table 17.3 Some RIS‐based models.

Table 17.4 Some RIS‐aided systems relying on machine learning techniques.

Table 17.5 Parameters in receive signal equation in DL RIS partitioning.

List of Illustrations

Chapter 1

Figure 1.1 Common meta‐material reflecting unit designs:. (a) split‐ring res...

Figure 1.2 RIS model.

Figure 1.3 Baseband model of digital communication system in finite bandwidt...

Figure 1.4 Baseband model of digital communication system in RIS supported w...

Figure 1.5 RIS‐supported wireless communication system.

Figure 1.6 Use‐case scenario of RIS in outdoor environment.

Figure 1.7 Use‐case scenario of RIS in indoor environment.

Figure 1.8 Comparison of the PE performance of SISO, SIMO systems under flat...

Figure 1.9 Comparison of the upper bound PE performance between SIMO flat fa...

Figure 1.10 MGF approach‐based APE for different values of in RIS.

Figure 1.11 PDF approach‐based APE for different values of in RIS.

Figure 1.12 PE performance comparison between the continuous and discrete ph...

Figure 1.13 Outage performance of RIS‐aided SISO system under varying .

Chapter 2

Figure 2.1 RIS‐a new network node in the communication chain.

Figure 2.2 RIS‐assisted communication for coverage enhancement.

Figure 2.3 Coverage extension by cascading RIS.

Figure 2.4 Energy‐focusing use cases of RIS.

Figure 2.5 Energy‐nulling use cases of RIS.

Figure 2.6 Multiplexing and interference management using RIS in conjunction...

Figure 2.7 Multiple access using RIS.

Figure 2.8 Energy harvesting using RIS.

Figure 2.9 WPT using RIS.

Figure 2.10 RIS for secure communication.

Figure 2.11 Localization accuracy using RIS.

Figure 2.12 Hybrid RIS‐aided cell‐free network.

Figure 2.13 RIS‐aided IoT applications.

Figure 2.14 RIS‐aided D2D communication.

Figure 2.15 RIS‐aided VANETs.

Figure 2.16 RIS‐aided UAVs.

Figure 2.17 RIS‐aided SWIPT.

Chapter 3

Figure 3.1 PRIS.

Figure 3.2 ARIS.

Figure 3.3 Hybrid RIS.

Figure 3.4 Contiguous RIS.

Figure 3.5 Discrete RIS.

Figure 3.6 Reflective mode of RIS.

Figure 3.7 Refraction mode of RIS.

Figure 3.8 Transmitting mode of RIS.

Figure 3.9 Receiving mode of RIS.

Figure 3.10 A scenario of TDD communication employing the same set of phase ...

Figure 3.11 A scenario of TDD communication using a separate set of phase sh...

Figure 3.12 RIS‐aided FDD transmission.

Figure 3.13 A scenario of the FD mode at RIS.

Figure 3.14 RIS‐aided wireless transmission.

Figure 3.15 Spatial correlation in RIS‐assisted communication.

Figure 3.16 Spatial correlation coefficient values of RIS with and element...

Figure 3.17 Spatial correlation coefficient values of RIS with and element...

Figure 3.18 Spatial correlation coefficient values of RIS with and element...

Figure 3.19 Distribution density fit for and .

Figure 3.20 Geometrical structure of a single RIS cell.

Figure 3.21 Finite RIS.

Figure 3.22 Infinite RIS.

Figure 3.23 Reflection coefficient magnitude against incidence angle.

Figure 3.24 Reflection coefficient phase against incidence angle.

Figure 3.25 Probability of error for planar RIS with various inter‐element...

Figure 3.26 Probability of error for planar RIS with Diversity factor .

Chapter 4

Figure 4.1 MIMO system.

Figure 4.2 M‐MIMO system.

Figure 4.3 The propagation of spherical waves becoming planar after the Rayl...

Figure 4.4 A gray line depicting the distance between a device and an elemen...

Figure 4.5 A gray line depicting the distance between two elements from para...

Figure 4.6 Representations of RIS‐aided M‐MIMO communication systems. (a) op...

Figure 4.7 An RIS‐aided M‐MIMO communication system with random user positio...

Figure 4.8 Comparison of different optimization techniques, demonstrating th...

Figure 4.9 Comparison of different optimization techniques, revealing the av...

Figure 4.10 Comparison of different optimization techniques, demonstrating t...

Figure 4.11 Comparison of an RIS‐aided M‐MIMO communication system with rand...

Figure 4.12 Comparison of an RIS‐aided M‐MIMO communication system with rand...

Figure 4.13 Comparison of an RIS‐aided M‐MIMO communication system with rand...

Figure 4.14 An RIS‐aided M‐MIMO communication system featuring fixed and str...

Figure 4.15 Comparison of different optimization techniques, demonstrating t...

Figure 4.16 Comparison of different optimization techniques, demonstrating t...

Figure 4.17 Comparison of different optimization techniques, demonstrating t...

Figure 4.18 An RIS‐aided M‐MIMO communication system featuring fixed and str...

Figure 4.19 Comparison of different optimization techniques, demonstrating t...

Figure 4.20 Comparison of different optimization techniques, demonstrating t...

Figure 4.21 Comparison of different optimization techniques, demonstrating t...

Chapter 5

Figure 5.1 N‐RIS assisted MU‐MIMO downlink transmission system.

Figure 5.2 Precoding for N‐RIS‐MU‐MIMO systems.

Figure 5.3 The flowchart with all processes involved in conducting the exper...

Figure 5.4 N‐RIS‐MU‐MIMO, BER performance comparison for a different number ...

Figure 5.5 N‐RIS‐MU‐MIMO, BER performance comparison for a different number ...

Figure 5.6 N‐RIS‐MU‐MIMO, BER performance comparison for a different number ...

Figure 5.7 N‐RIS‐MU‐MIMO, BER performance comparison for a different number ...

Figure 5.8 ML‐based CSI estimation procedure for RIS communication systems....

Figure 5.9 Scheme for ML‐based beamforming estimation procedure for RIS comm...

Figure 5.10 Deep‐reinforcement learning scheme for beamforming in RIS‐aided ...

Figure 5.11 Scheme of federated learning in multiple RIS MU‐MIMO communicati...

Figure 5.12 SER performance for DL‐based detection schemes in RIS communicat...

Chapter 6

Figure 6.1 The simulated RIS‐aided ‐user M‐MIMO communication scenario, wit...

Figure 6.2 The adopted RIS‐aided ‐user M‐MIMO communication simplified two‐...

Figure 6.3 Average EE versus the transmit budget power [dBm] at the BS (). ...

Figure 6.4 Average EE versus the total number of reflective elements at the ...

Figure 6.5 CDF of the amplitude gain () for transmit budget power of 10 dBm...

Figure 6.6 Percentage of budget power utilized versus transmit budget power....

Figure 6.7 Average EE versus number of iterations for the proposed algorithm...

Chapter 7

Figure 7.1 IRS basic architecture. 

Figure 7.2 IRS‐assisted communication system.

Figure 7.3 IRS elements uniform phase grouping.

Figure 7.4 IRS elements double phase grouping.

Figure 7.5 IRS elements random grouping.

Figure 7.6 IRS elements grouping.

Figure 7.7 IRS‐aided communication network.

Figure 7.8 IRS‐assisted network setup for simulation.

Figure 7.9 Impact of bits.

Figure 7.10 Impact of .

Figure 7.11 Impact of grouping.

Figure 7.12 CDF and PDF variation with IRS grouping.

Chapter 8

Figure 8.1 Use cases of RIS.

Figure 8.2 RIS‐AP‐NOMA system.

Figure 8.3 Outage performance of smart RIS‐AP‐NOMA user 2 for different valu...

Figure 8.4 Outage performance of smart RIS‐AP‐NOMA user 1 for different valu...

Figure 8.5 Outage performance of blind RIS‐AP‐NOMA user 2 for different valu...

Figure 8.6 Outage performance of blind RIS‐AP‐NOMA user 1 for different valu...

Figure 8.7 Comparison of outage of user 2 for NOMA, blind RIS‐AP‐NOMA and sm...

Figure 8.8 Comparison of outage of user 1 for NOMA, blind RIS‐AP‐NOMA and sm...

Figure 8.9 ABER performance of a smart RIS‐AP‐NOMA user 2.

Figure 8.10 ABER performance of a smart RIS‐AP‐NOMA user 1.

Figure 8.11 ABER performance of a blind RIS‐AP‐NOMA user 2.

Figure 8.12 ABER performance of a blind RIS‐AP‐NOMA user 1.

Figure 8.13 ABER comparison of conventional NOMA, smart and blind RIS‐AP‐NOM...

Figure 8.14 ABER comparison of conventional NOMA, smart and blind RIS‐AP‐NOM...

Chapter 9

Figure 9.1 Visual depiction of a network model featuring a two‐user setup in...

Figure 9.2 Illustration for RIS‐aided massive MIMO system.

Figure 9.3 A communication system for uplink transmission that integrates SM...

Figure 9.4 An illustrative scenario demonstrating the application of a small...

Figure 9.5 RIS‐aided mmW transmission with beamforming.

Figure 9.6 Block diagram illustration of VLC system model.

Figure 9.7 An RIS‐assisted indoor VLC system illustration.

Figure 9.8 RIS‐assisted UAV communications between a user and a BS.

Figure 9.9 An RIS mounted onto a UAV to act as a mobile relay to communicati...

Chapter 10

Figure 10.1 Positioning technology innovations and advancements toward 6G.

Figure 10.2 Emerging applications of RIS in 6G.

Figure 10.3 RIS‐aided localization network.

Figure 10.4 Fundamental localization principles and the RIS‐aided localizati...

Figure 10.5 State‐of‐the‐art technologies in RIS‐aided 6G–IoT networks.

Figure 10.6 Challenges of RIS‐based localization in 6G–IoT.

Chapter 11

Figure 11.1 Security attacks on RIS‐assisted wireless communication systems....

Figure 11.2 Security attacks on RIS‐assisted wireless communication systems....

Figure 11.3 Threats and attacks in RIS‐aided wireless network system.

Chapter 12

Figure 12.1 Deep reinforcement learning (DRL).

Figure 12.2 Applications of DRL in wireless communication.

Figure 12.3 Integrating DRL with RIS for enhanced wireless communication.

Figure 12.4 DRL algorithms for RIS optimization.

Figure 12.5 The proposed DRL(M‐S) algorithm architecture.

Figure 12.6 The collaborative operation of the proposed DRL (M‐S) algorithm ...

Figure 12.7 Rewards over time steps at ,  dB and  dB, respectively.

Chapter 13

Figure 13.1 An IRS‐aided wireless communication system.

Figure 13.2 Different models for channel modeling.

Figure 13.3 Detailed information about the different channel models.

Figure 13.4 System model: hybrid relay‐IRS‐aided system model.

Figure 13.5 EE versus user distance.

Figure 13.6 System model: IRS‐assisted MIMO system model.

Figure 13.7 System model: cooperative relay‐IRS aided ITS model.

Figure 13.8 EE versus SNR.

Figure 13.9 IRS‐NOMA communication system.

Chapter 14

Figure 14.1 Deep neural network.

Figure 14.2 DNN in RIS‐empowered wireless systems.

Figure 14.3 Autoencoder structure.

Figure 14.4 ‐Learning versus deep ‐learning.

Figure 14.5 DDPG‐based DTDE algorithm.

Figure 14.6 Deep reinforcement learning example.

Figure 14.7 System model for RIS‐empowered edge DRL system.

Chapter 15

Figure 15.1 Schematic of an RIS.

Figure 15.2 Uplink RIS‐aided wireless system.

Figure 15.3 Eavesdropping model.

Figure 15.4 RIS‐based secure resource allocation.

Figure 15.5 RIS‐based secure beamforming.

Figure 15.6 RIS‐based secure AN injection.

Figure 15.7 RIS‐based secure cooperative relaying.

Figure 15.8 RIS‐based secure cooperative jamming.

Chapter 16

Figure 16.1 Metasurfaces achieved using (a) varactor diodes and (b) PIN diod...

Figure 16.2 Propagation channel of RIS‐empowered wireless communication syst...

Figure 16.3 An incident wave reflected by an

a

 × 

b

RIS elem...

Figure 16.4 Generic 3D model of an RIS‐empowered wireless communication syst...

Figure 16.5 Architecture of a reconfigurable intelligent surface.

Figure 16.6 Practical methods of acquiring CSI for terrestrial RIS architect...

Figure 16.7 Aerial RIS‐empowered ground‐air‐ground communication systems bas...

Figure 16.8 Aerial RIS‐empowered ground–air–ground communication systems bas...

Figure 16.9 RIS‐empowered satellite communication system.

Figure 16.10 Multilayered integrated RIS‐empowered NTNs.

Figure 16.11 Integrated satellite‐aerial‐terrestrial RIS‐empowered communica...

Chapter 17

Figure 17.1 RIS‐assisted multi‐pair communication system diagram.

Figure 17.2 The cooperative RIS system discussed.

Figure 17.3 A basic analog beamforming model.

Figure 17.4 A basic digital beamforming model.

Figure 17.5 DDPG actions discussed.

Figure 17.6 A partitioned RIS array.

Figure 17.7 System model of two RISs system.

Figure 17.8 Impact on the performance of overall achievable rates by varying...

Figure 17.9 Achievable rates versus a different number of reflecting element...

Guide

Cover

Table of Contents

Series Page

Title Page

Copyright

Dedication

About the Editors

About the Contributors

Preface

Acknowledgments

Introduction

Begin Reading

Index

End User License Agreement

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Moeness Amin

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Desineni Subbaram Naidu

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Brian Johnson

Yi Qian

Adam Drobot

Hai Li

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James Duncan

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Behzad Razavi

Hugo Enrique Hernandez Figueroa

Joydeep Mitra

Thomas Robertazzi

Albert Wang

Patrick Chik Yue

Reconfigurable Intelligent Surfaces for 6G and Beyond Wireless Networks

Edited by

Agbotiname Lucky ImoizeUniversity of LagosNigeria

Vinoth Babu KumaraveluVellore Institute of TechnologyIndia

Dinh‐Thuan DoUniversity of Mount UnionAllianceUSA

Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication Data Applied for:

Hardback ISBN: 9781394250110

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Cover Image: © ArtSpace0714/Shutterstock

In loving memory of my late sister, Sarah Afebuame (Née Imoize).

Agbotiname Lucky Imoize

To my lovely wife, Dr. Arthi Murugadass, for her unwavering support and inspiration, and to my beloved daughters, Keshika Sruthi V. A. and Akshara V. A., for bringing endless joy and meaning to my life.

Vinoth Babu Kumaravelu

In memory of my parents, Do Tan and Tran Thi Bich Nga.

Dinh‐Thuan Do

About the Editors

Agbotiname Lucky Imoize received his Bachelors in Engineering (Honours) in Electrical and Electronics Engineering from Ambrose Alli University, Nigeria, in 2008, and MSc degree in the same field from the University of Lagos in 2012. He is a lecturer in the Department of Electrical and Electronics Engineering at the University of Lagos, Nigeria. Before joining the University of Lagos, he was a lecturer at Bells University of Technology, Nigeria. He has been a research scholar at the Ruhr University Bochum, Germany, under the sponsorship of the Nigerian Petroleum Technology Development Fund (PTDF) and the German Academic Exchange Service (DAAD) through the Nigerian‐German Postgraduate Program. From 2017 to 2018, he was a Fulbright Fellow, conducting as a visiting scholar at the Wireless@VT Laboratory, Bradley Department of Electrical and Computer Engineering, Virginia Tech., USA, under the supervision of Professor R. Michael Buehrer. He worked as a Core Network Products Manager at ZTE, Nigeria, and a Network Switching Subsystem Engineer at Globacom, Nigeria. His research interests include 6G wireless communications, wireless security systems, and artificial intelligence. He is the vice chair of the IEEE Communication Society Nigeria chapter, a registered engineer with the Council for the Regulation of Engineering in Nigeria, and a member of the Nigerian Society of Engineers. He is a senior member of IEEE.

Vinoth Babu Kumaravelu is presently working as a professor in the Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. In 2014, he received his PhD in MIMO‐OFDM‐based wireless communication from the Vellore Institute of Technology. In 2009, he graduated with an MTech in Communication Engineering from the same institution. During his MTech degree, he won a gold medal. He was also awarded merit scholarships twice during his master's degree at Vellore Institute of Technology for securing high grades. His areas of interest are wireless communications, digital communications, and signal processing, including spatial modulation, RIS, NOMA, wireless sensor networks, the Internet of Things, device‐to‐device communication, small cells, and vehicular ad hoc networks. He is the author of the books Communication Engineering and Digital Communications, published by Magnus Publications, India. He has been a guest editor for special issues in prestigious journals, including those published by Wiley and the Frontiers. He has successfully guided six PhD students and is currently mentoring four others in their doctoral research. He is a Senior Member of IEEE and an active member of the IEEE Communications Society.

Dinh‐Thuan Do is an assistant professor in the School of Engineering at the University of Mount Union, Alliance, Ohio, USA. He was a research scientist at the Electrical Engineering Department, University of Colorado Denver, Denver, USA. Also, he was formerly a research scientist in the Department of Electrical and Computer Engineering at the University of Texas at Austin, USA. Prior to joining the University of Texas at Austin, he was an assistant professor at Asia University in Taiwan and a research assistant professor at Ton Duc Thang University in Vietnam. His research interests include signal processing in wireless communications networks, nonorthogonal multiple access, full‐duplex transmission, and reconfigurable intelligent surfaces (RIS). He received the Golden Globe Award from the Vietnam Ministry of Science and Technology in 2015 (awarded to the top ten excellent scientists nationwide). He is currently serving as an Editor of Computer Communications, Associate Editor of EURASIP Journal on Wireless Communications and Networking, Associate Editor of Electronics, Associate Editor of ICT Express, and Editor of KSII Transactions on Internet and Information Systems. His publications include over 100 SCIE/SCI‐indexed journal articles and over 40 international conference papers. Additionally, he is the author of two textbooks and six book chapters, and he holds a PhD degree in Communications Engineering from Vietnam National University (VNU‐HCMC), Vietnam. He is a senior member of IEEE.

About the Contributors

Taufik Abrão SM'12, SM‐SBrT received the BS, MSc, and PhD degrees in electrical engineering from the Polytechnic School of the University of São Paulo, São Paulo, Brazil, in 1992, 1996, and 2001, respectively. Since March 1997, he has been with the Communications Group, Department of Electrical Engineering, Londrina State University, Paraná, Brazil, where he is currently an Associate Professor in Telecommunications and the Head of the Telecomm. & Signal Processing Lab. In 2018, he was with the Connectivity section, Aalborg University as a Guest Researcher, and with the Southampton Wireless Research Group in 2012 as an Academic Visitor. He has served as Associate Editor for the IEEE Transactions on Vehicular Technology, the IEEE Systems Journal, the IEEE Access, the IEEE Communication Surveys & Tutorials, the AEUe‐Elsevier, the IET Signal Processing, and JCIS‐SBrT, and as Executive Editor of the ETT‐Wiley (2016–2021) journal. His current research interests include communications systems, especially M‐MIMO, XL‐MIMO, RIS‐aided communication, optimization methods, machine learning, scheduling, estimation, resource allocation, and random access protocols.

Abel García Barrientos (IEEE, senior member) was born in Tenancingo, Tlaxcala, Mexico, in 1979. He received the Licenciatura degree in Electronics from the Autonomous University of Puebla, Mexico, in 2000, and MSc and PhD degrees in Electronics from the National Institute for Astrophysics, Optics, and Electronics (INAOE), Tonantzintla, Puebla, in 2003 and 2006, respectively. Since 2007, he has been a researcher at the Mechatronics Department at the Polytechnic University of Pachuca, Mexico. In 2009, he was a post‐doctoral fellow at the Micro‐ and Nano‐Systems Laboratory at McMaster University, Ontario, Canada. In 2010, he was a post‐doctoral fellow at the Advanced Materials and Device Analysis group of Institute for Microelectronics, Technische Universitat Wien, and in the summer of 2013, he was a visiting professor in the School of Physics & Astronomy at the University of Nottingham, UK. Since 2016, he has been a full‐time professor, level VI, in the Faculty of Science at the Universidad Autonoma de San Luis Potosi. His scientific interests include device simulation, semiconductor device modeling, high‐frequency electronics, and nanoelectronics. He has been a member of SNI‐Mexico from 2008 until 2022, level II. He is a fellow of the Mexican Academy of Science, the IEEE‐Hidalgo Subsection (Mexico), and he is also a member of the IEEE.

Nivetha Baskar received her Bachelor's degree in Electronics and Communication Engineering and Master's degree in Applied Electronics from the Anna University, Chennai, Tamil Nadu, India, in (2014‐2018) and (2018‐2020), respectively. She is currently a research scholar doing her Ph.D. in the Department of Communication Engineering, School of Electronics Engineering, at Vellore Institute of Technology, Vellore, Tamil Nadu, India. Her research interests include wireless communication, massive MIMO, resource allocation, and interference management.

Mehmet Bilim received his BSc, MSc, and PhD degrees in Electrical and Electronics Engineering from Erciyes University, Turkey, in 2010, 2012, and 2018, respectively. Currently, he is an associate professor in the Department of Electrical and Electronics Engineering at Niğde Ömer Halisdemir University, Turkey. He teaches courses in wireless communications, and his current research interests include spread spectrum communications, multiuser communications, multiple access techniques, wireless networks, millimeter wave communications, digital communications, fading channels, cooperative diversity, and applications of neural networks to communication systems. He was a recipient of the PhD Research Fellowships from the Scientific and Technological Research Council of Turkey (TUBITAK). He is also the author for more than 30 papers in major conferences and journals. He is a reviewer for IEEE, Elsevier, Wiley, Springer, and IET journals. Dr. Bilim currently serves as an editor of Frontiers in Communications and Networks.

Suganthi Evangeline Chakkaravarthy is an accomplished academic and researcher currently serving as an Associate Professor in the Department of Electronics and Communication Engineering at Sri Eshwar College of Engineering. She earned her PhD in Wireless Communications from Vellore Institute of Technology, Vellore, in 2022, following her ME from Coimbatore Institute of Technology in 2012 and her BE from C. Abdul Hakeem College of Engineering and Technology in 2010. Her primary research interests encompass advanced areas such as wireless communications, vehicular ad hoc networks (VANETs), blockchain technology, and the Internet of Things (IoT). She is an IEEE member.

Vinoth Kumar Chandra Babu received the Master's degree in Communication Engineering from the PSG College of Technology, Coimbatore, Tamil Nadu, India, in 2012. He is currently pursuing his PhD in the Department of Communication Engineering, School of Electronics Engineering, at Vellore Institute of Technology, Vellore, Tamil Nadu, India. He has also participated in various conferences, such as the International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN 2019) and the International Conference on Microelectronic Devices, Circuits and Systems (ICMDCS), organized by Vellore Institute of Technology. His research interests include blockchain and wireless communications.

Francisco Rubén Castillo‐Soria (Member, IEEE) received a Bachelor in Communications and Electronics and an M.S. degree in Telecommunications Engineering from the National Polytechnic Institute in Mexico City, Mexico, in 1999 and 2004, respectively. He received the Doctor of Science degree in Electronics and Telecommunications from the Center for Scientific Research and Higher Education of Ensenada (CICESE), Mexico, in 2015. Since 2017, he has been an Associate Professor at the Faculty of Sciences of the Autonomous University of San Luis Potosí, Mexico. His research interests include MIMO wireless communications, signal processing, spatial/index modulation, MBM, and AI for multiuser MIMO systems.

Sunil Chinnadurai received his M.S. in Electronics and Communication Engineering from the Mid Sweden University, Sweden, in 2012, and the PhD. in Electronics and Communication Engineering from Chonbuk National University, South Korea, in 2018. He was with the Signal Intelligence Research Center, Hanyang University, Seoul, South Korea, for a year, as a Postdoctoral Research Scientist. Since March 2019, he has been an Assistant Professor with SRM University AP, Amaravati, Andhra Pradesh, India. He has published many papers in reputed journals and conferences. His research interests include information theory, convex optimization, mathematical analysis, hyperspectral image processing, and optimization of signal processing algorithms for physical‐layer wireless communication systems. He received the Best Paper Award at the 24th MSPT International Symposium in 2016.

Wilson de Souza Junior received both the B.S. and M.Sc. degrees in Electrical Engineering from the State University of Londrina (UEL), Paraná, Brazil, in 2021 and 2023, respectively. Currently, he is actively pursuing his PhD degree at UEL, with a primary focus on research areas on resource allocation for RIS‐assisted massive MIMO, XL‐MIMO, and NOMA. Beyond this, his research interests extend to optimization theory and the application of machine learning in emerging technologies, encompassing ISAC, cooperative communication, relaying, symbiotic systems, SWIPT for URLLC and mMTC scenarios, all within the context of 5G and beyond 5G communication networks.

José Alberto del Puerto‐Flores received his MSc and PhD degrees in Electrical Engineering with a specialization in Telecommunications from CINVESTAV‐IPN, Guadalajara, Mexico, in 2014 and 2019, respectively. He is currently a research professor at Universidad Panamericana, Guadalajara campus. His research interests include signal and image processing, digital wireless communications, SISO and MIMO system design, channel estimation, linear and nonlinear data detection, channel estimation for vehicular communications, and modeling and simulation of nonstationary mobile wireless channels, as well as neural networks and AI‐based algorithms.

Unwana Macaulay Ekpe received his BE degree in Electrical and Electronic Engineering from the University of Uyo, Nigeria, in 2004. He was awarded the M Sc degree in Satellite Communications Engineering and the PhD in Electronic Engineering by the University of Surrey, Guildford, United Kingdom, in 2017 and 2012, respectively. Dr. Ekpe started his academic career in 2006 as a graduate assistant at the Akwa Ibom State University, Ikot Akpaden, Nigeria. He was promoted to senior lecturer in 2016 and headed the Department of Electrical and Electronic Engineering from October 2018 to September 2022. He is now an associate professor at Akwa Ibom State University and a visiting senior lecturer at the Department of Electrical and Computer Engineering, Makerere University, Kampala, Uganda. Dr. Ekpe's research interests include optimization of land‐mobile communication networks, virtualization of sixth‐generation nonterrestrial networks, and grid integration of renewable energy resources. Dr. Ekpe has supervised and graduated over 60 postgraduate and undergraduate students and has won several research grant awards. He has authored two book chapters and over 20 technical articles in peer‐reviewed international journals and conference proceedings.

Vishnu Vardhan Gudla is currently working as a 5G physical layer expert in Comms., Media & Tech Unit, L&T Technology Services, Bangalore. He has completed his M.Tech and Ph.D. in the field of wireless communication at Vellore Institute of Technology, Vellore. His doctoral research focused on exploring the potential technologies for next‐generation wireless communication networks, with a particular emphasis on developing spectral and energy‐efficient index/spatial modulation‐based MIMO and massive MIMO systems for the next‐generation networks. During his doctoral studies, he acquired a Senior Research Fellowship (SRF) from the Council of Scientific and Industrial Research (CSIR), India from April 2019 to April 2021. He has four years of Teaching Experience and four years of Research Experience. His current research focus includes developing novel physical layer algorithmic solutions for beyond 5G and 6G technologies such as orthogonal time and frequency modulation (OTFS), integrated sensing and communication (ISAC), reconfigurable intelligent surfaces (RIS), non‐orthogonal multiple access (NOMA), and machine learning for wireless applications.

Velmurugan Periyakarupan Gurusamy Sivabalan (Senior Member, IEEE) received B.E. degree in Electronics and Communication Engineering from Tamil Nadu College of Engineering, Coimbatore, India in 2002, M.E. degree in Applied Electronics from Kumaraguru College of Technology, Coimbatore in 2007 and the Ph.D. degree in Information and Communication Engineering from Anna University, in 2015. He is currently an Associate Professor in the Department of Electronics and Communication, Thiagarajar College of Engineering, Madurai. His research interests include Signal Processing for Wireless Communication, Reconfigurable Intelligent Surface, Radar Signal Processing, Convex Optimization and Software Defined Radio based analytics.

Carlos Adrián Gutiérrez Diaz de León received his BE. degree in Electronics and Digital Communication Systems from the Universidad Autónoma de Aguascalientes, Mexico, in 2002; Advanced Studies Diploma in Signal Processing and Communication Theory from the Universidad Politecnica de Cataluña, Spain, in 2005; MS degree in Electronics and Telecommunications from CICESE, Mexico, in 2006; and PhD in Mobile Communication Systems from the University of Agder, Norway, in 2009. From 2009 to 2011, he was with the School of Engineering, Universidad Panamericana, Aguascalientes, Mexico. Since January 2012, he has been with the Faculty of Sciences, Universidad Autonoma de San Luis Potosi, México. His research interests include modeling, simulation, and measurement of wireless channels; antenna design; electromagnetic wave propagation; vehicular communications; and radio sensing for vehicular applications and human activity recognition. Dr. Gutierrez has also served as an expert evaluator for the European Commission and CONACYT (Mexico); an associate editor for the IEEE Open Journal of Vehicular Technology, the IEEE Vehicular Technology Magazine; and a guest editor for several international journals. His publications received three best paper awards. He is a member of the Mexican National System of Researchers (level II), the Technical Committee on Propagation of the IEEE Vehicular Technology Society (VTS), and the Chair of the Propagation and Channel Modeling Theory Subcommittee of the IEEE VTS.

Kien Ho is a physics student at Chu Van An High School, Vietnam, and has been involved in various STEM initiatives. He is pursuing research in the fields such as computer security and machine learning.

Roilhi Frajo Ibarra‐Hernández is a postdoctoral researcher at the Faculty of Sciences of the Autonomous University of San Luis Potosi, specializing in machine learning for the enhancement of wireless communication systems. His current research project focuses on developing deep learning models for next‐generation wireless networks and signal processing, more specifically, the enhancement and signal detection of systems aided by reconfigurable intelligent surfaces (RIS).

Gaurav Jaiswal has been a Software Test Validation Engineer at NXP Semiconductor, Pune, since November 2020. Previously, he worked as a Validation and Integration Engineer at Qualcomm, Chennai, from May 2018 to October 2020. He holds an MTech in Communication Engineering from Vellore Institute of Technology, Vellore, and a BE in Electronics and Communication Engineering from IIST, Indore. His areas of interest include playing table tennis, volleyball, swimming, and Wi‐Fi technology.

Thiruvengadam Sundarrajan Jayaraman (Senior Member, IEEE) received his BE in Electronics and Communication Engineering from the Thiagarajar College of Engineering, Madurai, India, in 1991; his ME in Applied Electronics from the College of Engineering, Guindy, Chennai, India, in 1994; and his PhD from Madurai Kamaraj University, Madurai, in 2005. From January 2008 to December 2008, he was a Visiting Associate Professor in the Department of Electrical Engineering, Stanford University, Stanford, CA, USA, under a Postdoctoral Fellowship from the Department of Science and Technology, Government of India. He is currently a Professor and Dean (Academics) in the Department of Electronics and Communication Engineering, Thiagarajar College of Engineering. His research interests include statistical signal processing, MIMO wireless communications, reconfigurable intelligent surface, and near field communication.

Helen Sheeba John Kennedy received her M.E. in Applied Electronics and her B.E. in Electronics and Communication Engineering from the Thanthai Periyar Government Institute of Technology, Vellore, Tamil Nadu, India. She is currently pursuing her Ph.D. in the Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. Her research interests include reconfigurable intelligent surfaces, non‐orthogonal multiple access and its variants, machine learning for wireless networks, and cooperative communication.

Yasin Kabalcı received his BSc and PhD degrees in Electrical and Electronics Engineering from Erciyes University, Turkiye, in 2009 and 2015, respectively. He is currently a full professor and the head of the Telecommunication Division as well as the head of the Modern Communication Systems Research Group, Department of Electrical and Electronics Engineering, Niğde Ömer Halisdemir University, Turkiye. His research interests include 5G and beyond communication systems, mmWave communications, power‐line communication systems, error‐correcting codes, smart grids, smart grid communications, remote monitoring, IoT, and optimization methods.

Nuri Kapucu received his BSc, MSc, and PhD degrees in Electrical and Electronics Engineering from Erciyes University, Türkiye, in 2010, 2012, and 2017, respectively. From 2011 to 2018, he was a research assistant in the Department of Electrical and Electronics Engineering at Erciyes University. Currently, he is an associate professor in the Department of Electrical and Electronics Engineering at Hitit University. His current research interests include performance analysis over fading channels, reconfigurable intelligent surfaces, cooperative communications, millimeter‐wave communications, and analysis of modulation schemes. He serves as a reviewer for IEEE, Elsevier, Wiley, Springer, and IET journals, and he is currently a review editor for Frontiers in Communications and Networks.

Tuan Minh La is an active member of the Society of Vietnamese Young Scholars and conducts research with the WICOM lab at the University of Mount Union, Ohio, USA. His research interests include Reconfigurable Intelligent Surfaces, Tiny Machine Learning (Tiny ML), and the Internet of Things (IoT).

Sharon Macias‐Velasquez received her DSc in Industrial Engineering from the Autonomous University of Baja California, Mexico, in 2020. She is currently a postdoctoral researcher in the Mechanical and Electrical Engineering department at the Faculty of Engineering of Autonomous University of de San Luis Potosi, Mexico. Dr. Macias is a member of the National System of Researchers in México. Her research interests include cognitive ergonomics, virtual reality, human–computer interaction, and communication systems.

José Carlos Marinello Filho received his BS and MS degree in Electrical Engineering (the first with Summa Cum Laude) from Londrina State University, PR, Brazil, in Dec. 2012 and Sept. 2014, respectively, and his PhD from Polytechnic School of São Paulo University, São Paulo, Brazil, in Aug. 2018. From 2015 to 2019, he was an Assistant Professor with Londrina State University, and, since 2019, he has been an Adjunct Professor with Federal University of Technology PR, Cornálio Procápio, Brazil. His research interests include signal processing and wireless communications, especially massive MIMO/XL‐MIMO precoding/decoding techniques, acquisition of channel‐state information, multicarrier modulation, cross‐layer optimization of MIMO/OFDM systems, interference management in 5G, massive machine type communications, and random access protocols for crowded networks. He has been serving as Associate Editor for the SBrT Journal of Communications and Information Systems since 2019.

Arthi Murugadass is presently working as an Associate Professor Grade 2 in the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India. She completed her PhD from the Vellore Institute of Technology, Vellore, India, in 2017, and her ME (CSE) and BE (CSE) from Anna University, Chennai, India, in 2012 and 2010, respectively. She has 14 SCI, 39 Scopus indexed, and 25 international and national conference publications and 4 book chapter publications. She is one of the authors of the books Communication Engineering and Digital Communications, published by Magnus Publications. She is an AWS‐certified cloud practitioner. She has delivered invited talks and guest lectures on various latest domain topics. She is a reviewer in various reputed journals like MDPI, Springer, and Hindawi. She has more than 13 years of teaching and research experience. She has experience in different administrative roles as the head of the department, NBA coordinator, and BoS member. She is presently working in the areas of data science, blockchain technology, machine learning, and heterogeneous wireless networks.

Hieu Tri Nguyen is a high school student at Nguyen Sieu High School, located in Hanoi, Vietnam. As a young scholar, he is passionate about computational data science and electronics engineering.

Minh Dang Nguyen is currently an active member of the Society of Vietnamese Young Scholars. He is pursuing research topics in cutting‐edge fields such as reconfigurable intelligent surfaces (RIS), machine learning (ML), and artificial intelligence (AI).

Rafael Augusto Pedriali received his BS degree in Electrical Engineering from the State University of Maringá (2015), followed by a Master's (2019) and a PhD (2023) in Electrical Engineering from the School of Electrical and Computer Engineering (FEEC) at the State University of Campinas, UNICAMP. He is currently a postdoctoral researcher in the joint graduate program in Electrical Engineering at the State University of Londrina (UEL) and the Federal Technological University of Paraná, Cornélio Procópio campus. Additionally, he is an assistant professor at UEL, focusing on telecommunications‐related subjects in the Electrical Engineering program. In his field of research, he possesses extensive expertise in mobile radio channels, massive MIMO technology, RIS and ISAC systems, linear and nonlinear optimization, and machine learning.

Khoi Nguyen Phan is a student at the British International School, Ho Chi Minh City. He is currently pursuing research in multimodel machine learning, specifically those involving reinforcement learning, and is a runner of an AI bootcamp in Vietnam. He has contributed to multiple open‐source coding projects in the past.

Mukkara Prasanna Kumar received his BTech degree in Electronics and Communication Engineering from Yogi Vemana University, Kadapa, in 2018. He completed his MTech in Electronics and Communication Engineering at Jawaharlal Nehru Technological University Ananthapuramu College of Engineering, Ananthapuramu, in 2021. He is currently pursuing his PhD under the supervision of Dr. Sunil Chinnadurai in the Department of Electronics and Communication Engineering, SRM University, Amaravathi, India. He worked as an assistant professor for a year at S.V. College of Engineering, Andhra Pradesh. His research interests include wireless communications, 5G, massive MIMO, IRS, NOMA, and machine learning.

Minh Tuan Pham is currently an active member of the Vietnamese Young Scholars Community. He is pursuing research in computer science, deep learning and Internet of Things (IoT), with a particular focus on artificial intelligence (AI).

Narushan Pillay received his MSc Engineering (cum laude) and PhD degrees in Wireless Communications from the University of KwaZulu‐Natal, Durban, South Africa, in 2008 and 2012, respectively. Since 2009, he has been with the University of KwaZulu‐Natal. Previously, he was with the Council of Scientific and Industrial Research (CSIR), Defence, Peace, Safety, and Security (DPSS), South Africa. He supervised several MSc Engineering and PhD students. His research interests include physical wireless communications, including spectrum sensing for cognitive radio and MIMO systems. He has published several papers in well‐known journals in the area of research. He is a National Research Foundation (NRF) Rated Researcher in South Africa.

Arjun Chakravarthi Pogaku was born in India. He received his master's degree from Asia University, Taiwan, where he is currently pursuing his PhD in the Department of Computer Science and Information Engineering. He is currently a member of the WICOM Laboratory, Asia University, which is led by Dr. Thuan. His research interest includes wireless and satellite communications.

Vetriveeran Rajamani received his PhD degree in Electronics Engineering from Chonbuk National University, South Korea, in 2018. He received his ME in VLSI Design and BE in ECE from Anna University, Chennai, in 2013 and 2010, respectively. He is working as an associate professor in the School of Electronics Engineering (SENSE), Department of Micro and Nano Electronics, Vellore Institute of Technology, Vellore, Tamil Nadu, India. He had published various research articles in SCI/SCIE journals, international conferences, and book chapters in India/abroad. He is also an active member in several professional societies. He has been awarded the Brain Korea (BK‐21) Doctoral Scholarship during the academic year 2014–2018. He was invited to give numerous guest lectures at various places in India/abroad on innovative topics. His areas of interest include modeling of memristors, analysis and design of memristive systems in electronic and neuromorphic circuits, analog/digital circuits design, wireless communication, digital signal processing, and applied electronics.

Dipinkrishnan Rayaroth received his ME in Communication Systems from Anna University, Coimbatore, Tamil Nadu, India, and his BTech in Electronics and Communication Engineering from the Cochin University of Science and Technology, Kerala, India. He is currently pursuing his PhD in the Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. His research interests include NOMA, digital communications, reconfigurable intelligent surfaces, and wireless communications.

Md. Abdul Latif Sarker received his PhD degree from Chonbuk National University, Republic of Korea, in 2016. He is currently working in a senior research position at the Center for ICT & Automotive Convergence at Kyungpook National University in Korea. Dr. Sarker was a participant in many projects, such as the Brain Korea‐21, a world‐class university, the ministry of education science and technology project, and the Samsung R&D university program. Dr. Sarker has served as a reviewer of various international journals, including IEEE Journal on Selected Areas in Communications, IEEE Access, IEEE System Journal, IEEE Communication Letters, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Computational Social Systems, and IEEE Transactions on Vehicular Technology, ICT Express, Cluster Computing, and so on. His main research interests include various topics in signal processing for wireless communications, millimeter wave technology, localization and sensing, reconfigurable intelligent surfaces, multisensory connected and autonomous vehicles, vehicular communication, optimization, and algorithm development.

Poongundran Selvaprabhu received his Bachelor's degree in Electronics and Communication Engineering from the Anna University, Chennai, Tamil Nadu, India, in 2009, and Master's degree in Electronics Design at Mid Sweden University, Sweden, in 2012. He completed his PhD in Wireless and Mobile Communications at Chonbuk National University, South Korea, in 2017. He worked as a postdoctoral Research Fellow in the Division of Electronics and Information Engineering at Chonbuk National University and Inha University, South Korea (2017‐2018). He is currently working as an Associate Professor, Vellore Institute of Technology, Vellore, Tamil Nadu, India. Dr. Poongundran was a participant in many projects such as Brain Korea 21 (BK21), World Class University (WCU), Ministry of Education Science and Technology (MEST) project, which were funded by NRF, Republic of Korea. His main research interests include various topics in 5G wireless communications, especially interference alignment for massive MIMO, signal processing, information theory, wireless body area network (WBAN) and NOMA with a focus on energy optimization, algorithm design, and artificial intelligence (AI)‐machine learning.

Anjana Babu Sujatha has completed her M.Tech in Communication Engineering from the Vellore Institute of Technology (VIT), Vellore, Tamil Nadu. She has received her B.Tech in Electronics and Communication Engineering from the LBS College of Engineering, Kasaragod, Kerala. Currently, she is pursuing her Ph.D. in the Department of Communication Engineering, School of Electronics Engineering, at VIT, Vellore. Her research interests include next‐generation wireless communication techniques like reconfigurable intelligent surfaces, non‐orthogonal multiple access, and millimeter‐wave communication.

Ammar Summaq received his BTech degree in Electronics and Communication Engineering from Damascus University in 2010. He completed his MTech in Electronics and Communication Engineering at KIIT University in 2021. He is currently pursuing his PhD under the supervision of Dr. Sunil Chinnadurai in the Department of Electronics and Communication Engineering, SRM University, Amaravathi, India. His research interests include wireless communications, 5G, IRS, and machine learning.

Samarendra Nath Sur (Senior Member, IEEE) received his MTech in Digital Electronics and Advanced Communication from Sikkim Manipal University, in 2012, and his PhD in MIMO Signal Processing from the National Institute of Technology (NIT), Durgapur, in 2019. Since 2008, he has been associated with the Sikkim Manipal Institute of Technology, India, where he is currently an Assistant Professor (SG) in the Department of Electronics and Communication Engineering. He has published over 120 SCI/Scopus‐indexed international journals and conference papers. His current research interests include wireless communications, non‐orthogonal multiple access (NOMA), energy harvesting (EH), intelligent reflecting surface (IRS), the Internet of Things (IoT) remote sensing, and radar image/signal processing (soft computing). He is a member of the IEEE‐IoT and Institution of Engineers, India (IEI). He is also serving as an Associate Editor for International Journal on Smart Sensing and Intelligent Systems (S2IS) (SCOPUS, ESCI). Additionally, he had the privilege of serving as the Guest Editor for topical collections and special issues in reputable journals published by Springer Nature, MDPI, and Hindawi.

Dang Ngoc Thien Nguyen