On performance evaluation of cooperative spectrum sensing in cognitive radio networks

Gang Xiong, Shalinee Kishore, Aylin Yener

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Scopus citations

    Abstract

    This paper presents the system level performance evaluation for energy-detection based cooperative spectrum sensing in cognitive radio networks. Three performance criteria are quantitively analyzed for cooperative spectrum sensing. First, the average error probability is determined given fixed amplifier gains for a fixed number of secondary users by considering all possible channel realizations. Second, the asymptotic error probability is computed in a power constrained cognitive radio network when the number of secondary user approaches infinity. Third, the outage probability is examined when instantaneous error probability is greater than a predefined threshold. In all three calculations, both additive white Gaussian noise (AWGN) and Rayleigh fading assumptions are used to capture the observation and fusion channels. Numerical results indicate that in order to maintain a desired detection performance in low and moderate fusion signal to noise ratio (SNR) regimes, fusion channels need to be as reliable as possible, while local received SNRs can be dynamic and provide spatial diversity. Moreover, it is shown that under AWGN observation channels and Rayleigh fading fusion channels, a diversity order equal to the number of secondary users can be achieved.

    Original languageEnglish (US)
    Title of host publication2010 44th Annual Conference on Information Sciences and Systems, CISS 2010
    DOIs
    StatePublished - 2010
    Event44th Annual Conference on Information Sciences and Systems, CISS 2010 - Princeton, NJ, United States
    Duration: Mar 17 2010Mar 19 2010

    Publication series

    Name2010 44th Annual Conference on Information Sciences and Systems, CISS 2010

    Other

    Other44th Annual Conference on Information Sciences and Systems, CISS 2010
    Country/TerritoryUnited States
    CityPrinceton, NJ
    Period3/17/103/19/10

    All Science Journal Classification (ASJC) codes

    • Information Systems
    • Information Systems and Management

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