Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise

Abstract

Traditional spectrum sensing techniques optimized against Gaussian noise may suffer from severe performance deterioration when non-Gaussian noise is present. To overcome such drawback, this paper proposes a novel Kendall's tau (KT) based detector for detecting the primary signal in additive non-Gaussian noise characterized by contaminated Gaussian model (CGM). The proposed detector, which exploits the ordinary information rather than the cardinal information from the observed raw data, is both computationally efficient and robust against large-valued outliers. The analytic expressions concerning the expectation and variance of KT are firstly established under this specific CGM, which emulates a frequently encountered scenario in practice. Performance analyses are further conducted in terms of false alarm probability, detection probability and receiver operating characteristic (ROC) curve. Comparative results with the traditional energy detection (ED) and three other state-of-the-art detectors demonstrate the superiority of KT, including: 1) robustness against impulsive noise compared to ED, 2) accurate control of the false alarm probability without any prior information about the noise distribution, and 3) better detection performance over three popular detectors with either Gaussian or non-Gaussian background noise.