Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks

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 distributed optimization techniques for wireless communications. He received a Certificate of Appreciation as an Exemplary Reviewer of the IEEE Communications Letters, in 2011 and 2012. He received the IEEE Signal Processing Society 2015 Young Author Best Paper Award and the Funds for Distinguished Young Scholars of Jiangsu Province in 2016.

Kun Xu received his B.S. degree in Communication Engineering and Ph. D. degree in Communications and Information Systems, both from PLA University of Science and Technology, in 2007 and 2013, respectively. He is currently a lecturer at the College of Information and Communication, National University of Defense Technology (NUDT). His research interests include HF communication, unmanned aerial vehicle communication, and relay communication.

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.

Offers new insights into how to model and exploit user demand in resource management

Introduction.- Learning the Optimal Network with Handoff Constraint: MAB RL Based Network Selection.- Learning the Optimal Network with Context Awareness: Transfer RL Based Network Selection.- Meeting Dynamic User Demand with Transmission Cost Awareness: CT-MAB RL Based Network Selection.- Meeting Dynamic User Demand with Handoff Cost Awareness: MDP RL Based Network Handoff.- Matching Heterogeneous User Demands: Localized Cooperation Game and MARL based Network Selection.- Exploiting User Demand Diversity: QoE game and MARL Based Network Selection.- Future Work.