Springer Stochastic Geometry Analysis of Multi-Antenna Wireless Networks

This book presents a unified framework for the tractable analysis of large-scale, multi-antenna wireless networks using stochastic geometry. This mathematical analysis is essential for assessing and understanding the performance of complicated multi-antenna networks, which are one of the foundations of 5G and beyond networks to meet the ever-increasing demands for network capacity. Describing the salient properties of the framework, which makes the analysis of multi-antenna networks comparable to that of their single-antenna counterparts, the book discusses effective design approaches that do not require complex system-level simulations. It also includes various application examples with different multi-antenna network models to illustrate the framework’s effectiveness. Review “The most remarkable aspect of the book is the reader-friendly structure and the style in which it has been written. This book is suitable for both beginners in wireless network analysis and experienced researchers who intend to work in depth on the performance analysis of multi-antenna wireless networks. The importance of this book both from the practical and from the theoretical standpoint is unquestionable. This book will be an essential part of every mathematical library.” (Viktor Ohanyan, zbMATH 1447.60004, 2020) From the Back Cover This book presents a unified framework for the tractable analysis of large-scale, multi-antenna wireless networks using stochastic geometry. This mathematical analysis is essential for assessing and understanding the performance of complicated multi-antenna networks, which are one of the foundations of 5G and beyond networks to meet the ever-increasing demands for network capacity. Describing the salient properties of the framework, which makes the analysis of multi-antenna networks comparable to that of their single-antenna counterparts, the book discusses effective design approaches that do not require complex system-level simulations. It also includes various application examples with different multi-antenna network models to illustrate the framework’s effectiveness.  About the Author Xianghao Yu received his B.Eng. degree in Information Engineering from Southeast University, Nanjing, China, in 2014, and his Ph.D. degree in Electronic and Computer Engineering from the Hong Kong University of Science and Technology (HKUST), Kowloon, Hong Kong, in 2018. His research interests include millimeter wave communications, MIMO systems, mathematical optimization, and stochastic geometry. He was the recipient of the IEEE GLOBECOM 2017 Best Paper Award. Chang Li received his B.Eng. degree in Information and Communication Engineering from Xi’an Jiaotong University, Xi’an, China, in 2010, and his Ph.D. degree in Electrical and Computer Engineering from the Hong Kong University of Science and Technology (HKUST), Kowloon, Hong Kong, in 2016. From February 2015 to January 2016, he was a Visiting Student at the Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. He is currently a guest researcher with the Advanced Network Technologies Division (ANTD), National Institute of Standards and Technology (NIST). His research interests include network localization and navigation, heterogeneous networks, MIMO systems, green communications, and the application of stochastic geometry. Jun Zhang received his Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin. He is currently an Assistant Professor at the Department of Electronic and Information Engineering at the Hong Kong Polytechnic University. Dr. Zhang co-authored the book Fundamentals of LTE (Prentice-Hall, 2010). He is a co-recipient of the 2016 Marconi Prize Paper Award in Wireless Communications, the 2014 Best Paper Award for the EURASIP Journal on Advances in Signal Processing, an IEEE GLOBECOM Best Paper Aw