Restless Multi-Armed Bandit in Opportunistic Scheduling
Autor Kehao Wang, Lin Chenen Limba Engleză Paperback – 21 mai 2022
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Specificații
ISBN-13: 9783030699611
ISBN-10: 3030699617
Pagini: 151
Ilustrații: XII, 151 p. 12 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.24 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030699617
Pagini: 151
Ilustrații: XII, 151 p. 12 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.24 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Introduction.- RMAB in Opportunistic Scheduling.- Optimality of Myopic Policy with Imperfect Sensing.- Whittle Index Policy with Imperfect Sensing.- Heuristic Policy with Imperfect Sensing.- Optimality of Myopic Policy with Imperfect Observation.- Whittle Index Policy for Multi-State Channel Scheduling.- Conclusion.
Notă biografică
Kehao Wang received the B.S degree in Electrical Engineering, M.S. degree in Communication and Information System from Wuhan University of Technology, Wuhan, China, in 2003 and 2006, respectively, and Ph.D in the Department of Computer Science, the University of Paris-Sud XI, Orsay, France, in 2012. From Feb. 2013 to Aug. 2013, he was a postdoc with the HongKong Polytechnic University. In 2013, he joined the School of Information Engineering at the Wuhan University of Technology. From 2015 to 2018, he had been a visiting scholar in the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA. USA. His research interests are stochastic optimization, operation research, scheduling, wireless network communications, and embedded operating system.
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.
Textul de pe ultima copertă
This book provides foundations for the understanding and design of computation-efficient algorithms and protocols for those interactions with environment, i.e., wireless communication systems. The book provides a systematic treatment of the theoretical foundation and algorithmic tools necessarily in the design of computation-efficient algorithms and protocols in stochastic scheduling. The problems addressed in the book are of both fundamental and practical importance. Target readers of the book are researchers and advanced-level engineering students interested in acquiring in-depth knowledge on the topic and on stochastic scheduling and their applications, both from theoretical and engineering perspective.
- 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.
Caracteristici
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