ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC

Distributed bayesian compressed spectrum sensing based on mutual information

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  • Author Bio:

    WANG Zhen-xing, born in 1985, master. Research field: cognitive radio. E-mail: 072021046@fudan.edu.cn

  • Corresponding author: YANG Tao
  • Received Date: 11 May 2009
  • Rev Recd Date: 28 August 2009
  • Publish Date: 31 October 2009
  • When compressive sensing is applied in cognitive radio network, spectrum sensing precision by every cognitive radio user is greatly different due to different channel environments between them.Consequently information fusion methods in network and the efficient data processing manner by compressive sensing can be combined to improve sensing precision.First, CS (compressive sampling) is performed independently by every cognitive radio user for rough sensing, and then the sensing information between different users is exchanged for their spatial diversity. Here, mutual information is taken as a measure to evaluate the sensing difference between two cognitive radio users, and those users with large difference are related. The sensing information of every cognitive radio user will be shared under this kind of relationship. After sensing information is shared, Bayesian inference for CS construction in every cognitive radio user is re-built to update the local sensing. The simulation results show that the proposed scheme has advantage both in sensing accuracy and in sensing speed over the conventional scheme.
    When compressive sensing is applied in cognitive radio network, spectrum sensing precision by every cognitive radio user is greatly different due to different channel environments between them.Consequently information fusion methods in network and the efficient data processing manner by compressive sensing can be combined to improve sensing precision.First, CS (compressive sampling) is performed independently by every cognitive radio user for rough sensing, and then the sensing information between different users is exchanged for their spatial diversity. Here, mutual information is taken as a measure to evaluate the sensing difference between two cognitive radio users, and those users with large difference are related. The sensing information of every cognitive radio user will be shared under this kind of relationship. After sensing information is shared, Bayesian inference for CS construction in every cognitive radio user is re-built to update the local sensing. The simulation results show that the proposed scheme has advantage both in sensing accuracy and in sensing speed over the conventional scheme.
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