Abstract
We study a classical opportunistic wireless link scheduling problem in cognitive radio networks with Signal to Interference plus Noise Ratio (SINR) constraints. Consider a collection of communication links, assume that each link has a channel state. The state transitions follow a transition rule. The exact state information of each link is not available due to the uncertainty of primary users’ activities. The expected channel state is predicted probabilistically by investigating its history and feedbacks when the channels are used. The objective is to pick communication links sequentially over a long time horizon to maximize the average reward. To the best of our knowledge, no prior work can satisfyingly provide solutions for the opportunistic wireless link scheduling problem when considering SINR constraints. In this work, we adopt the robust paradigm of restless multi-armed bandit for the problem and design an efficient algorithm. We analyze the performance via Lyapunov potential function and demonstrate that the proposed algorithm can achieve an approximation bound.
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Acknowledgements
The research of authors is supported in part by KSU OVPR grant, NSFC Grant No. 61802097 and No. 61572377, and the Project of Qianjiang Talent (Grant No. QJD1802020).
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Xu, X., Chen, Y., He, S., Bobbie, P.O. (2019). OWLS: Opportunistic Wireless Link Scheduling with SINR Constraints. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_34
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DOI: https://doi.org/10.1007/978-3-030-23597-0_34
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