Overview
- Offers new insights into how to model and exploit user demand in resource management
- Provides various application examples of reinforcement learning algorithms on resource management of wireless networks
- Presents novel game models and associated MARL algorithms
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Table of contents (8 chapters)
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Front Matter
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MAB RL Based Online Network Selection
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Front Matter
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MDP RL Based Online Network Selection
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Front Matter
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Game-Theoretic MARL Online Network Selection
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Front Matter
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Back Matter
Keywords
About this book
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks
Book Subtitle: A Reinforcement Learning Perspective
Authors: Zhiyong Du, Bin Jiang, Qihui Wu, Yuhua Xu, Kun Xu
DOI: https://doi.org/10.1007/978-981-15-1120-2
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-1119-6Published: 18 November 2019
Softcover ISBN: 978-981-15-1122-6Published: 18 November 2020
eBook ISBN: 978-981-15-1120-2Published: 06 November 2019
Edition Number: 1
Number of Pages: XII, 136
Number of Illustrations: 3 b/w illustrations, 42 illustrations in colour
Topics: Wireless and Mobile Communication, Computer Communication Networks, Communications Engineering, Networks, Information and Communication, Circuits