This book begins with discussing the fundamentals of Simultaneously Transmitting and Reflecting Surfaces (STARS) from the electromagnetic (EM) and communication perspectives. The basic signal and channel models for employing STARS in wireless communications is introduced as well. Then, the different categories and possible hardware implementation of STARS are highlighted. Next, the authors focus on attention to the STARS-aided wireless communications. The authors provide a comprehensive performance analysis of STARS with the outage probability, diversity gain and power scaling laws. Moreover, the operating protocols and corresponding beamforming design of STARS are discussed under different phase-shift models.

As a further advance, the application of machine learning tools in STAR-aided wireless communications is introduced for addressing the beamforming design and resource allocation problems. The novel STARS-aided integrated sensing and communications (ISAC) in future wireless networks are also discussed with several case studies. Within this book, readers will find an extensive exploration of the STARS concept. The content encompasses a thorough survey of STARS research, covering principles, implementation, performance evaluation and applications. By presenting a comprehensive review of the STARS family, this book serves as a valuable resource for gaining insight into the complete pipeline of STARS research. Finally, the authors conclude the book by highlighting several future research directions for STARS.

This book targets graduate, undergraduate, and postgraduate students as well as researchers working in wireless communications. Wireless communication engineers in industry and government will also want to purchase this book.

Provides an in-depth review of Simultaneously Transmitting and Reflecting Surfaces (STARS), including case studies Covers models, categories, hardware implementation, communication performance analysis and optimization applications Highlights several future research directions for STARS

Autorentext

Yuanwei Liu has been a (tenured) full Professor in Department of Electrical and Electronic Engineering (EEE) at The University of Hong Kong (HKU) since September, 2024. His research interests include non-orthogonal multiple access, reconfigurable intelligent surface, integrated sensing and communications, and machine learning. He is a Fellow of the IEEE, a Fellow of AAIA, a Web of Science Highly Cited Researcher, an IEEE Communication Society Distinguished Lecturer, an IEEE Vehicular Technology Society Distinguished Lecturer, the rapporteur of ETSI Industry Specification Group on Reconfigurable Intelligent Surfaces on work item of "Multi-functional Reconfigurable Intelligent Surfaces (RIS): Modelling, Optimisation, and Operation", and the UK representative for the URSI Commission C on "Radio communication Systems and Signal Processing". He was listed as one of 35 Innovators Under 35 China in 2022 by MIT Technology Review. He received IEEE ComSoc Outstanding Young Researcher Award for EMEA in 2020. He received the 2020 IEEE Signal Processing and Computing for Communications (SPCC) Technical Committee Early Achievement Award, IEEE Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award. He received IEEE ComSoc Outstanding Nominee for Best Young Professionals Award in 2021. He is the co-recipient of the 2024 IEEE Communications Society Heinrich Hertz Award, the Best Student Paper Award in IEEE VTC2022-Fall, the Best Paper Award in ISWCS 2022, the 2022 IEEE SPCC-TC Best Paper Award, the 2023 IEEE ICCT Best Paper Award, and the 2023 IEEE ISAP Best Emerging Technologies Paper Award. He serves as the Co-Editor-in-Chief of IEEE ComSoc TC Newsletter, an Area Editor of IEEE Communications Letters, an Editor of IEEE Communications Surveys & Tutorials, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Cognitive Communications and Networking, and IEEE Transactions on Communications (2018-2023). He serves as the (leading) Guest Editor for Proceedings of the IEEE on Next Generation Multiple Access, IEEE JSAC on Next Generation Multiple Access, IEEE JSTSP on Intelligent Signal Processing and Learning for Next Generation Multiple Access, and IEEE Network on Next Generation Multiple Access for 6G. He serves as the Publicity Co-Chair for IEEE VTC 2019-Fall, the Panel Co-Chair for IEEE WCNC 2024, Symposium Co-Chair for several flagship conferences such as IEEE GLOBECOM, ICC and VTC. He serves the academic Chair for the Next Generation Multiple Access Emerging Technology Initiative, vice chair of SPCC and Technical Committee on Cognitive Networks (TCCN).

Xidong Mu has been a lecturer (an assistant professor) with the Centre for Wireless Innovation (CWI), Queen's University Belfast, U.K. since August 2024. His research interests include non-orthogonal multiple access (NOMA), reconfigurable intelligent surface aided communications, integrated sensing and communications, semantic communications, and optimization theory. He received the IEEE ComSoc Outstanding Young Researcher Award for EMEA region in 2023, the Exemplary Reviewer Certificate of the IEEE Transactions on Communications in 2020 and 2022, and the IEEE Communication Letters in 2021-2023. He is the recipient of the 2024 IEEE Communications Society Heinrich Hertz Award, the Best Paper Award in ISWCS 2022, the 2022 IEEE SPCC-TC Best Paper Award, and the Best Student Paper Award in IEEE VTC2022-Fall. He serves as the Public Engagement and Social Networks Coordinator of IEEE ComSoC SPCC Technical Committee, the secretary of the IEEE ComSoc Next Generation Multiple Access (NGMA) Emerging Technology Initiative and the Special Interest Group (SIG) in SPCC Technical Committee on Signal Processing Techniques for NGMA. He also serves as an Editor of IEEE Transactions on Communications, a Guest Editor for IE

Inhalt

Introduction.- Signal and Chanel Models of STARS.- Categories and Hardware Implementations of STARS.- Performance Analysis for STARS.- Operating Protocols and Beamforming Design for STARS.- Machine Learning Empowered STARS.- STARS-aided Integrated Sensing and Communications.- Research Directions and Conclusions.