Abstract
Background: Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. Objectives: Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum sensing with the assumption that the noise follows a non-Gaussian distribution with heavier tails. Methods/findings: A fuzzy logicbased method is proposed for primary user detection under non Gaussian Noise. The results are highlighted for the Laplacian noise. Through Monte Carlo simulations it is observed that Laplacian noise noticeably affects the performance of energy detector. Also, a fractional change in noise uncertainty degrades the performance of energy detector. The performance of the proposed scheme is presented through receiver operating characteristic (ROC) and plot of the detection probability versus signal-to-noise ratio (SNR) using simulations. It is shown that by appropriately choosing the membership functions and the fuzzy rule base in the fuzzy inference system the proposed fuzzy logic method for spectrum sensing provides reliable detection. Keywords: NonGaussian noise; Fuzzy logic; Spectrum sensing; noise uncertainty
Highlights
Majority of spectrum sensing schemes are focused on primary user detection in Gaussian noise
The Laplacian noise is an important non-Gaussian noise distribution and it is frequently used in engineering studies
The results provided are averaged over 10000 Monte Carlo simulations
Summary
Majority of spectrum sensing schemes are focused on primary user detection in Gaussian noise. The Gaussian distribution cannot be used to model Radio Frequency (RF) noise and low frequency atmospheric noise This is primarily due to the fact that the noise in practice is likely to generate observations of high magnitude than what can be produced from the Gaussian distribution. High magnitude observations are referred to as impulsive noise This conveys that the probability distribution function (pdf) of such noise has heavy tails. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user.
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