Restless Multi-Armed Bandit in Opportunistic Scheduling [E-Book] / by Kehao Wang, Lin Chen.
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...
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Personal Name(s): | Wang, Kehao, author |
Chen, Lin, author | |
Edition: |
1st edition 2021. |
Imprint: |
Cham :
Springer,
2021
|
Physical Description: |
XII, 151 pages 12 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783030699598 |
DOI: |
10.1007/978-3-030-69959-8 |
Subject (LOC): |
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505 | 0 | |a 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. | |
520 | |a 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. | ||
650 | 0 | |a Computational intelligence. | |
650 | 0 | |a Machine learning. | |
650 | 0 | |a Telecommunication. | |
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