Spectrum Sensing For Cognitive Radios Through Differential Entropy
Journal Title: EAI Endorsed Transactions on Cognitive Communications - Year 2016, Vol 2, Issue 6
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.
Authors and Affiliations
Sanjeev Gurugopinath, R. Muralishankar, H. N. Shankar
Keywords
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- EP ID EP45641
- DOI http://dx.doi.org/10.4108/eai.5-4-2016.151147
- Views 253
- Downloads 0
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