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)

Abstract

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 publicationConference proceedings
Subtitle of host publicationIEEE Wireless Communications and Networking Conference, WCNC 2013
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages89-94
ISBN (Electronic)978-1-4673-5939-9
ISBN (Print)978-1-4673-5938-2
DOIs
Publication statusPublished - 2013
MoE publication typeNot Eligible
EventIEEE Wireless Communications and Networking Conference, WCNC 2013 - Shanghai, China
Duration: 7 Apr 201310 Apr 2013

Conference

ConferenceIEEE Wireless Communications and Networking Conference, WCNC 2013
Abbreviated titleWCNC 2013
CountryChina
CityShanghai
Period7/04/1310/04/13

Fingerprint

Intelligent agents
Cognitive radio
Learning algorithms
Network protocols

Cite this

Chen, X., Zhao, Z., Grace, D., & Zhang, H. (2013). Reciprocal learning for cognitive medium access. In Conference proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2013 (pp. 89-94). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/WCNC.2013.6554544
Chen, Xianfu ; Zhao, Zhifeng ; Grace, David ; Zhang, Honggang. / Reciprocal learning for cognitive medium access. Conference proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2013. Institute of Electrical and Electronic Engineers IEEE, 2013. pp. 89-94
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Chen, X, Zhao, Z, Grace, D & Zhang, H 2013, Reciprocal learning for cognitive medium access. in Conference proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2013. Institute of Electrical and Electronic Engineers IEEE, pp. 89-94, IEEE Wireless Communications and Networking Conference, WCNC 2013, Shanghai, China, 7/04/13. https://doi.org/10.1109/WCNC.2013.6554544

Reciprocal learning for cognitive medium access. / Chen, Xianfu; Zhao, Zhifeng; Grace, David; Zhang, Honggang.

Conference proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2013. Institute of Electrical and Electronic Engineers IEEE, 2013. p. 89-94.

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

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AB - 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.

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Chen X, Zhao Z, Grace D, Zhang H. Reciprocal learning for cognitive medium access. In Conference proceedings: IEEE Wireless Communications and Networking Conference, WCNC 2013. Institute of Electrical and Electronic Engineers IEEE. 2013. p. 89-94 https://doi.org/10.1109/WCNC.2013.6554544