Overview
- Introduces Restless Multi-Armed Bandit (RMAB) and presents its relevant tools involved in machine learning and how to adapt them for application
- Elaborates on research bringing the conventional decision theory and stochastic optimal technology into wireless communication applications involving machine learning
- Delivers a comprehensive treatment on problems ranging from theoretical modeling and analysis, to practical algorithm design and optimization
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Table of contents (6 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
Lin Chen is a professor in the School of Data and Computer Science at Sun Yat-sen University, which he joined in 2019. He received his B. Sc. degree in Electrical Engineering in 2002 from Southeast University, his M. Sc. in Networking in 2005 from University of Paris 6, and his Engineer Diploma and Ph. D. in Computer Science and Networking in 2005 and 2008 from Telecom ParisTech (ENST). He received his Habilitation thesis at University of Paris-Sud in 2017. He was an associate professor at the Department of Computer Science at University of Paris-Sud from 2009 to 2019. His research is focused on distributed algorithms and protocols in emerging networked systems, with particular emphasis on energy efficiency, resilience, and security.
Bibliographic Information
Book Title: Restless Multi-Armed Bandit in Opportunistic Scheduling
Authors: Kehao Wang, Lin Chen
DOI: https://doi.org/10.1007/978-3-030-69959-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-69958-1Published: 20 May 2021
Softcover ISBN: 978-3-030-69961-1Published: 21 May 2022
eBook ISBN: 978-3-030-69959-8Published: 19 May 2021
Edition Number: 1
Number of Pages: XII, 151
Number of Illustrations: 12 illustrations in colour
Topics: Communications Engineering, Networks, Computational Intelligence, Machine Learning