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Record Nr. |
UNINA9910484135903321 |
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Autore |
Wang Kehao |
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Titolo |
Restless multi-armed bandit in opportunistic scheduling / / Kehao Wang, Lin Chen |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (XII, 151 p. 12 illus. in color.) |
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Disciplina |
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Soggetti |
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Wireless communication systems - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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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