Restless Multi-Armed Bandit in Opportunistic Scheduling / / by Kehao Wang, Lin Chen

• Record Nr.: UNINA9910484135903321
• Autore: Wang Kehao
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• ISBN: 3-030-69959-5
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (XII, 151 p. 12 illus. in color.)
• Disciplina: 621.384131
• Soggetti: Telecommunication; Computational intelligence; Machine learning; Communications Engineering, Networks; Computational Intelligence; Machine Learning
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.