Reciprocity inspired learning for opportunistic spectrum access in cognitive radio networks

Xianfu Chen, Tao Chen, W. Cheng, H. Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    2 Citations (Scopus)

    Abstract

    This paper addresses opportunistic spectrum access (OSA) in non-cooperative cognitive radio networks (CRNs). The selfish behaviors of the secondary users (SUs) will cause a CRN to collapse. The SUs are thus enabled to build beliefs about how other SUs would respond to their decision makings. The interaction among the SUs is modeled as a stochastic learning process. In this way, each SU can independently learn the behaviors of the competitors, optimize the OSA strategies, and finally achieve the goal of reciprocity. Two learning algorithms are proposed to stabilize the stochastic CRNs, the convergence properties of which are also proven theoretically. Simulation results validate the performance of the proposed results, and show that the achieved system performance outperforms some existing protocols.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publication8th International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2013
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages202-207
    ISBN (Electronic)978-1-4799-2120-1
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    Event8th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2013 - Washington, United States
    Duration: 8 Jul 201310 Jul 2013
    Conference number: 8

    Conference

    Conference8th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2013
    Abbreviated titleCROWNCOM 2013
    Country/TerritoryUnited States
    CityWashington
    Period8/07/1310/07/13

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