Reciprocal learning for cognitive medium access

Xianfu Chen, Zhifeng Zhao, David Grace, Honggang Zhang

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

    3 Citations (Scopus)


    This paper considers designing efficient medium access strategies for secondary users (SUs) to select frequency channels to sense and access in cognitive radio networks. The interaction among the SUs is considered as a learning problem, in which every SU behaves as an intelligent agent. Each SU believes that its competitors alter their future medium access strategies in proportion to its own current strategy change. These beliefs adapt in accordance with limited information exchange. In this way, each SU can obtain the behavior feature of other users through conjecture, optimize the medium access strategy, and finally achieve the goal of reciprocity, based on which two learning algorithms are proposed. We show that the SUs' stochastic behaviors and beliefs converge to a steady state under some conditions. Numerical results are provided to evaluate the performance of the two algorithms, and show that the achieved system performance gain outperforms some existing protocols.
    Original languageEnglish
    Title of host publicationIEEE Wireless Communications and Networking Conference, WCNC 2013
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4673-5939-9, 978-1-4673-5937-5
    ISBN (Print)978-1-4673-5938-2
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    EventIEEE Wireless Communications and Networking Conference, WCNC 2013 - Shanghai, China
    Duration: 7 Apr 201310 Apr 2013

    Publication series

    SeriesIEEE Wireless Communications and Networking Conference


    ConferenceIEEE Wireless Communications and Networking Conference, WCNC 2013
    Abbreviated titleWCNC 2013


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