This book provides a comprehensive review and in-depth study on efficient beamforming design and rigorous performance analysis in mmWave networks, covering beam alignment, beamforming training and beamforming-aided caching. Due to significant beam alignment latency between the transmitter and the receiver in existing mmWave systems, this book proposes a machine learning based beam alignment algorithm for mmWave networks to determine the optimal beam pair with a low latency. Then, to analyze and enhance the performance of beamforming training (BFT) protocol in 802.11ad mmWave networks, an analytical model is presented to evaluate the performance of BFT protocol and an enhancement scheme is proposed to improve its performance in high user density scenarios. Furthermore, it investigates the beamforming-aided caching problem in mmWave networks, and proposes a device-to-device assisted cooperative edge caching to alleviate backhaul congestion and reduce content retrieval delay. This book concludes with future research directions in the related fields of study. The presented beamforming designs and the corresponding research results covered in this book, provides valuable insights for practical mmWave network deployment and motivate new ideas for future mmWave networking. This book targets researchers working in the fields of mmWave networks, beamforming design, and resource management as well as graduate students studying the areas of electrical engineering, computing engineering and computer science. Professionals in industry who work in this field will find this book useful as a reference.

Auteur
Peng Yang received his B.E. degree in Communication Engineering and Ph.D. degree in Information and Communication Engineering from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2013 and 2018, respectively. He was with the Department of Electrical and Computer Engineering, University of Waterloo, Canada, as a Visiting Ph.D. Student from Sept. 2015 to Sept. 2017, and a Postdoctoral Fellow from Sept. 2018 to Dec. 2019. Since 2020, he has been an Associate Professor with the School of Electronic Information and Communications, HUST. His current research focuses on wireless networking, mobile edge computing, video streaming and analytics.
Wen Wu received the B.E. degree in Information Engineering from South China University of Technology, Guangzhou, China, and the M.E. degree in Electrical Engineering from University of Science and Technology of China, Hefei, China, in 2012 and 2015, respectively. He received the Ph.D. degree in Electrical and Computer Engineering from University of Waterloo, Waterloo, ON, Canada, in 2019. Starting from 2019, he works as a Post-doctoral fellow with the Department of Electrical and Computer Engineering, University of Waterloo. His research interests include millimeter-wave networks and AI-empowered wireless networks.
Ning Zhang received the B.Sc. degree from Beijing Jiaotong University, Beijing, China, the M.Sc. degree from Beijing University of Posts and Telecommunications, Beijing, China, and the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 2007, 2010, and 2015, respectively. After that, he was a postdoc research fellow at University of Waterloo and University of Toronto, Canada, respectively. He is now an Associate Professor at University of Windsor, Canada. His research interests include vehicular and wireless networking, mobile edge computing, and security. He serves as an Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Cognitive Communications and Networking, and IET Communications. He also serves/served as a TPC chair for IEEE VTC-Fall 2021 and IEEE SAGC 2020, a general chair for IEEE SAGC 2021, and a track/symposium chair for several international conferences and workshops, such as IEEE ICC and IEEE VTC. He has been a senior member of IEEE since 2018.

Xuemin Shen received the B.Sc. degree from Dalian Maritime University, Dalian, China, in 1982, and the M.Sc. and Ph.D. degrees from Rutgers University, New Brunswick, NJ, USA, in 1987 and 1990, respectively, all in Electrical Engineering. He is currently a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Canada. His research focuses on network resource management, wireless network security, Internet of Things, 5G and beyond, and vehicular ad hoc and sensor networks. Dr. Shen is a registered Professional Engineer of Ontario, Canada, an Engineering Institute of Canada Fellow, a Canadian Academy of Engineering Fellow, a Royal Society of Canada Fellow, a Chinese Academy of Engineering Foreign Member, and a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Dr. Shen received the R.A. Fessenden Award in 2019 from IEEE, Canada, Award of Merit from the Federation of Chinese Canadian Professionals (Ontario) in 2019, James Evans Avant Garde Award in 2018 from the IEEE Vehicular Technology Society, Joseph LoCicero Award in 2015 and Education Award in 2017 from the IEEE Communications Society, and Technical Recognition Award from Wireless Communications Technical Committee (2019) and AHSN Technical Committee (2013). He has also received the Excellent Graduate Supervision Award in 2006 from the University of Waterloo and the Premier's Research Excellence Award (PREA) in 2003 from the Province of Ontario, Canada. He served as the Technical Program Committee Chair/Co-Chair for IEEE Globecom'16, IEEE Infocom'14, IEEE VTC'10 Fall, IEEE Globecom'07, and the Chair for the IEEE Communications Society Technical Committee on Wireless Communications. Dr. Shen is the elected IEEE Communications Society Vice President for Technical & Educational Activities, Vice President for Publications, Member-at-Large on the Board of Governors, Chair of the Distinguished Lecturer Selection Committee, Member of IEEE ComSoc Fellow Selection Committee. He was/is the Editor-in-Chief of the IEEE IoT JOURNAL, IEEE Network, IET Communications, and Peer-to-Peer Networking and Applications.

Contenu
Introduction.- Literature Review of mmWave Networks.- Machine Learning Based Beam Alignment in mmWave Networks.- Beamforming Training Protocol Design and Analysis.- Beamforming-Aided Cooperative Edge Caching in mmWave Dense Networks.- Summary and Future Directions.