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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https://doi.org/10.1007/978-3-030-69959-8Opportunistic scheduling; Restless bandit; Optimality; Myopic policy; Whittle index

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978-3-030-69961-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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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 ..

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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

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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.

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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.

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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.