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.
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.
Description based on print version record. Includes bibliographical references and index.