A Sequential Learning Framework for Resource Management in Wireless Networks

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Due to the scarcity of wireless spectrum and growing demands for wireless data services, resource management is a perpetual problem in wireless systems with very diverse characteristics from metropolitan scale networks to personal area networks. When the knowledge of system states and parameters is unknown or incomplete, sequential learning or specifically the multi-armed bandit framework offers an elegant solution to balance the trade-offs in exploitation (practicing the best-known decisions thus far) and exploration (finding out the utility of other decisions).****The primary goal of my research program is to advance the fundamental understanding and development of efficient robust solutions for cyber physical systems, and wireless and mobile data networks. The specific objective of the project is to develop a sequential learning framework for a variety of resource management problems in wireless networks. The contribution of the project will be three-fold. First, we will advance the theory of sequential learning and devise new algorithms to address practical challenges arising from wireless networks. Second, we will apply the proposed sequential learning methodologies to develop effective solutions to resource management problems in network monitoring, cognitive radio spectrum access and routing in delay tolerant networks. Third, through testbed development and system implementation, this project will offer HQP involved training opportunities not only in algorithm design and analysis but also hand-on experiences with state-of-the-art wireless network hardware and software.****