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Efficient Algorithm for Multi-Constrained Opportunistic Wireless Scheduling

The onset of wireless networks globally has thrust researchers in academia and industry to solve problems related to this ever-growing field. In this paper, we study the multi-constrained opportunistic wireless scheduling problem in cognitive radio networks. Given a collection of secondary user communication links, the channel state of each link is unknown due to the unpredictable primary users’ activities, but can be estimated by exploring the channel state transitions and channel state feedback. A scheduling algorithm is used to decide a subset of links to transmit each time with both interference-free constraints and power budget constraints. The objective of this paper is to design a scheduling algorithm to optimize the average reward over a long time horizon. Current existing approaches cannot satisfyingly provide solutions for the wireless opportunistic scheduling problem when considering multiple constraints. In this work, we adopt the paradigm of the restless multi-armed bandit and propose a fast and simple approximation algorithm. The performance of the proposed algorithm is verified with a small approximation bound for the multi-constrained wireless opportunistic wireless scheduling problem.