Springer Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks: A Reinforcement Learning Perspective

This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond. From the Back Cover This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond. About the Author Zhiyong Du received his B.S. degree in Electronic Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and his Ph.D. degree in Communications and Information Systems from the College of Communications Engineering, PLA University of Science and Technology, Nanjing, China, in 2015. He is currently a lecturer at the National University of Defense Technology. His research interests include 5G, quality of experience (QoE), learning theory, and game theory. Bin Jiang received his B.S. degree in Communication Engineering and Ph.D. degree in Information and Communication Engineering both from the National University of Defense Technology, Changsha, China, in 1996 and 2006, respectively. He is currently a Professor at the National University of Defense Technology. His research interests include 5G, artificial intelligence, and wireless signal processing. Qihui Wu received his B.S., M.S., and Ph.D. degrees in Communications and Information Systems from the PLA University of Science and Technology, Nanjing, China, in 1994, 1997, and 2000, respectively. He is Professor at the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. His current research interests include algorithms and optimization for cognitive wireless networks, software-defined radio, and wireless communication systems. Yuhua Xu received his B.S. degree in Communication Engineering and Ph.D. degree in Communications and Information Systems from the College of Communications Engineering, PLA University of Science and Technology, in 2006 and 2014, respectively. He is currently an Associate Professor at the College of Communications Engineering, Army Engineering University of PLA. He has published several papers in international conferences and respected journals. His research interests include UAV communication networks, opportunistic spectrum access, learning theory, and distrib