8th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

Reciprocity Inspired Learning for Opportunistic Spectrum Access in Cognitive Radio Networks

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2013.252895,
        author={Tao Chen and Xianfu Chen and Wei Cheng and Honggang Zhang},
        title={Reciprocity Inspired Learning for Opportunistic Spectrum Access in Cognitive Radio Networks},
        proceedings={8th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={ICST},
        proceedings_a={CROWNCOM},
        year={2013},
        month={11},
        keywords={cognitive radio opportunistic spectrum access reinforcement learning},
        doi={10.4108/icst.crowncom.2013.252895}
    }
    
  • Tao Chen
    Xianfu Chen
    Wei Cheng
    Honggang Zhang
    Year: 2013
    Reciprocity Inspired Learning for Opportunistic Spectrum Access in Cognitive Radio Networks
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2013.252895
Tao Chen, Xianfu Chen1,*, Wei Cheng2, Honggang Zhang3
  • 1: VTT Technical Research Centre of Finland
  • 2: The George Washington University
  • 3: Supelec France
*Contact email: xianfu.chen@vtt.fi

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.