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Restless Multi-Armed Bandit in Opportunistic Scheduling [electronic resource] / by Kehao Wang, Lin Chen.

By: Wang, Kehao [author.].
Contributor(s): Chen, Lin [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XII, 151 p. 12 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030699598.Subject(s): Telecommunication | Computational intelligence | Machine learning | Communications Engineering, Networks | Computational Intelligence | Machine LearningAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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