This book provides comprehensive insights into the theory, models, and unique characteristics of intelligent reflecting surface (IRS) and its appealing applications in wireless information and power transmission for B5G/6G. It is organized as follows:

Chapter 1 gives an overview of the fundamentals of IRS, including the signal and channel models of IRS, its hardware architecture, advantages and practical constraints. Chapter 2, 3 and 4 focus on how to optimize the joint design of active and passive beamforming for three types of IRS-aided wireless information/energy transmission systems, namely IRS-aided power transfer (WPT), IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication networks (WPCN). Chapter 2 specifically presents a general framework design for IRS-aided WPT systems from the perspectives of energy beamforming design and channel estimation, while the dynamic IRS beamforming design is also discussed.

Chapter 3 thoroughly studies the IRS-aided SWIPT for achieving better R-E trade-off, wherein two different designs based on weighted sum-power maximization at energy users (EUs) and under individual quality-of-service (QoS), requirements at both information users (IUs) and EUs are investigated.

Chapter 4 presents three paradigms of IRS-aided WPCN, i.e., IRS-aided half-duplex (HD) WPCN, IRS-aided full-duplex (FD) WCPN and IRS-aided multiple-input-multiple-output (MIMO) FD WPCN. The authors present some related emerging topics, e.g., active IRS aided wireless communications and IRS-aided unmanned aerial vehicle (UAV) communications in chapter 5.

This book targets postgraduate students studying electrical engineering and computer science, and can be used as a secondary textbook. Professionals, researchers and engineers working in wireless networks will also find this book useful as a reference.

Auteur

Qingqing Wu is an Associate Professor with Shanghai Jiao Tong University. His current research interest includes intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV) communications, and MIMO transceiver design. He has coauthored more than 100 IEEE journal papers with 29 ESI highly cited papers and 9 ESI hot papers, which have received more than 20,000 Google citations. He was listed as the Clarivate ESI Highly Cited Researcher in 2022 and 2021, the Most Influential Scholar Award in AI-2000 by Aminer in 2021 and World's Top 2% Scientist by Stanford University in 2020 and 2021. He was the recipient of the IEEE Communications Society Fred Ellersick Prize, IEEE Best Tutorial Paper Award in 2023, Asia-Pacific Best Young Researcher Award and Outstanding Paper Award in 2022, Young Author Best Paper Award in 2021, the Outstanding Ph.D. Thesis Award of China Institute of Communications in 2017, the IEEE ICCC Best Paper Award in 2021, and IEEE WCSP Best Paper Award in 2015. He serves as an Associate Editor for IEEE Transactions on Communications, IEEE Communications Letters, IEEE Wireless Communications Letters. He was the Lead Guest Editor for IEEE Journal on Selected Areas in Communications. Xinrong Guan received both the B.Eng. degree in Communications Engineering and the Ph.D. degree in Communications and Information Systems from the College of Communications Engineering, PLA University of Science and Technology, Nanjing, China in 2009 and 2014, respectively. From 2014, he worked as a lecturer at the College of Communications Engineering, Army Engineering University of PLA. His current research interests include physical layer security, wireless key generation, and intelligent reflecting surface (IRS). Meng Hua received Ph.D. degree in School of Information Science and Engineering, Southeast University, Nanjing, China, in 2021. From 2021-2023, he was working at the State Key Laboratory of Internet of Things for Smart City, University of Macau as a Postdoc. He was the recipient of the Outstanding Ph.D. Thesis Award of Chinese Institute of Electronics in 2021. His current research interests include localization, integrated sensing and communication, and intelligent reflecting surface.