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Titlebook: Restless Multi-Armed Bandit in Opportunistic Scheduling; Kehao Wang,Lin Chen Book 2021 The Editor(s) (if applicable) and The Author(s), un

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发表于 2025-3-21 18:48:05 | 显示全部楼层 |阅读模式
书目名称Restless Multi-Armed Bandit in Opportunistic Scheduling
编辑Kehao Wang,Lin Chen
视频video
概述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 decisi
图书封面Titlebook: Restless Multi-Armed Bandit in Opportunistic Scheduling;  Kehao Wang,Lin Chen Book 2021 The Editor(s) (if applicable) and The Author(s), un
描述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.
出版日期Book 2021
关键词Opportunistic scheduling; Restless bandit; Optimality; Myopic policy; Whittle index
版次1
doihttps://doi.org/10.1007/978-3-030-69959-8
isbn_softcover978-3-030-69961-1
isbn_ebook978-3-030-69959-8
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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发表于 2025-3-21 20:34:33 | 显示全部楼层
https://doi.org/10.1007/978-3-030-69959-8Opportunistic scheduling; Restless bandit; Optimality; Myopic policy; Whittle index
发表于 2025-3-22 00:55:29 | 显示全部楼层
978-3-030-69961-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
发表于 2025-3-22 06:44:58 | 显示全部楼层
Conclusion and Perspective,This book addresses a special kind of restless multiarmed bandit problem arising in opportunistic scheduling with imperfect sensing or observation conditions where each channel evolves as a discrete-time two-state Markovian chain in Chaps. . and . and multistate Markovian chain in Chaps. . and ..
发表于 2025-3-22 11:20:35 | 显示全部楼层
Kehao Wang,Lin ChenIntroduces 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 decisi
发表于 2025-3-22 16:34:46 | 显示全部楼层
Myopic Policy for Opportunistic Scheduling: Homogeneous Two-State Channels, RMAB problem. Specifically, for a family of generic and practically important utility functions, we establish the closed-form conditions to guarantee the optimality of the myopic policy even under imperfect sensing.
发表于 2025-3-22 19:47:58 | 显示全部楼层
Whittle Index Policy for Opportunistic Scheduling: Heterogeneous Two-State Channels,ic structures of the underlying nonlinear dynamic evolving system, based on which we devise the linearization scheme for each region to establish indexability and compute the Whittle index for each region.
发表于 2025-3-23 00:15:44 | 显示全部楼层
发表于 2025-3-23 05:04:42 | 显示全部楼层
发表于 2025-3-23 07:58:07 | 显示全部楼层
Myopic Policy for Opportunistic Scheduling: Homogeneous Multistate Channels,alysis on the performance of myopic policy, introduce monotone likelihood ratio (MLR) order to characterize the evolving structure of belief information, and establish a set of closed-form conditions to guarantee the optimality of the myopic scheduling policy.
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