Spectrum Sensing Over Class-A Impulsive Noise
Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing opportunistic usage of the frequency bands that are not heavily occupied by licensed users. Spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, two spectrum sensing techniques, energy detection and eigenvalue based detection, are introduced. The performance of both methods is evaluated in the presence of class-A impulsive noise. The performance of both methods is evaluated in terms of probability of detection and probability of false alarm. Numerical results show that the performance of both detection techniques is worse over impulsive fading channel than that over AWGN and impulsive non-fading channels.
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Document Type: Research Article
Publication date: March 1, 2016
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