mca 14(4): e1

Research Article

Cooperation Scheme For Distributed Spectrum Sensing In Cognitive Radio Networks

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  • @ARTICLE{10.4108/mca.1.4.e1,
        author={Ying Dai and Jie Wu},
        title={Cooperation Scheme For Distributed Spectrum Sensing In Cognitive Radio Networks},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        keywords={Cognitive radio networks (CRNs), spectrum sensing, game theory, USRP testbed.},
  • Ying Dai
    Jie Wu
    Year: 2014
    Cooperation Scheme For Distributed Spectrum Sensing In Cognitive Radio Networks
    DOI: 10.4108/mca.1.4.e1
Ying Dai1, Jie Wu1
  • 1: Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122


Spectrum sensing is an essential phase in cognitive radio networks (CRNs). It enables secondary users (SUs) to access licensed spectrum, which is temporarily not occupied by the primary users (PUs). The widely used scheme of spectrum sensing is cooperative sensing, in which an SU shares its sensing results with other SUs to improve the overall sensing performance, while maximizing its throughput. For a single SU, if its sensing results are shared early, it would have more time for data transmission, which improves the throughput. However, when multiple SUs send their sensing results early, they are more likely to send out their sensing results simultaneously over the same signaling channel. Under these conditions, conflicts would likely happen. Then, both the sensing performance and throughput would be affected. Therefore, it is important to take when-to-share into account. We model the spectrum sensing as an evolutionary game. Different from previous works, the strategy set for each player in our game model contains not only whether to share its sensing results, but also when to share. The payoff for each player is defined based on the throughput, which considers the influence of the time spent both on sensing and sharing. We prove the existence of the evolutionarily stable strategy (ESS). In addition, we propose a practical algorithm for each secondary user to converge to the ESS. We conduct experiments on our testbed consisting of 4 USRP N200s. The experimental results verify for our model, including the convergence to the ESS.