EAI Endorsed Transactions on Cognitive Communications 16(6): e5

Research Article

Spectrum Sensing For Cognitive Radios Through Differential Entropy

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  • @ARTICLE{10.4108/eai.5-4-2016.151147,
        author={Sanjeev Gurugopinath and R. Muralishankar and H. N. Shankar},
        title={Spectrum Sensing For Cognitive Radios Through Differential Entropy},
        journal={EAI Endorsed Transactions on Cognitive Communications},
        volume={16},
        number={6},
        publisher={EAI},
        journal_a={COGCOM},
        year={2016},
        month={4},
        keywords={Spectrum sensing, goodness-of-fit, differential entropy, maximum entropy principle, non-Gaussian noise.},
        doi={10.4108/eai.5-4-2016.151147}
    }
    
  • Sanjeev Gurugopinath
    R. Muralishankar
    H. N. Shankar
    Year: 2016
    Spectrum Sensing For Cognitive Radios Through Differential Entropy
    COGCOM
    EAI
    DOI: 10.4108/eai.5-4-2016.151147
Sanjeev Gurugopinath1, R. Muralishankar2,*, H. N. Shankar3
  • 1: Department of Electronics and Communication Engineering, PES University, Bengaluru 560085.
  • 2: Department of Electronics and Communication Engineering, CMR Institute of Technology, Bengaluru 560037.
  • 3: Department of Electrical and Electronics Engineering, CMR Institute of Technology, Bengaluru 560037.
*Contact email: muralishankar@cmrit.ac.in

Abstract

In this work, we present a novel Goodness-of-Fit Test driven by differential entropy for spectrum sensing in cognitive radios, under three different noise models – Gaussian, Laplacian and mixture of Gaussians. We analyze the proposed detector under Gaussian noise which models the worst-case. We then analyze by considering the Laplacian noise process which has tails heavier than that of the Gaussian. We generalize the analysis considering the noise to be a mixture of Gaussians, which is often the case with noise and interference in communication systems. We analyze the performance under each of these cases for a large class of practically relevant fading channel models and primary signal models, with emphasis on low Signal-to-Noise ratio regimes. Towards this end, we derive closed form expressions for the distribution of the test statistic under the null hypothesis and the detection threshold that satisfies a constraint on the probability of false-alarm. Through Monte Carlo simulations, we demonstrate that our detection strategy outperforms an existing spectrum sensing technique based on order statistics.