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Title: Learning Temporal–Spatial Spectrum Reuse
Authors: Zhang, Yi
Tay, Wee Peng
Li, Kwok Hung
Esseghir, Moez
Gaiti, Dominique
Keywords: Cognitive radio
Spectrum reuse
Issue Date: 2016
Source: Zhang, Y., Tay, W. P., Li, K. H., Esseghir, M., & Gaiti, D. (2016). Learning Temporal–Spatial Spectrum Reuse. IEEE Transactions on Communications, 64(7), 3092-3103.
Series/Report no.: IEEE Transactions on Communications
Abstract: We formulate and study a multi-user multi-armed bandit problem that exploits the temporal-spatial opportunistic spectrum access (OSA) of primary user channels, so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve an NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed policies that, however, have linear regrets. Our first distributed policy utilizes a distributed graph coloring and consensus algorithm to determine SUs' channel access ranks, while our second distributed policy incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. We show that all these policies have linear regrets in our temporal-spatial OSA framework. Simulations suggest that our proposed policies have significantly smaller regrets than the other policies when spectrum temporal-spatial reuse is allowed.
ISSN: 0090-6778
DOI: 10.1109/TCOMM.2016.2569093
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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