Hardback : $209.00
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.
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.
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.
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,
anEngineering 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.
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