Kendall's Tau Based Spectrum Sensing for Cognitive Radio in the Presence of Laplace Noise

In the presence of non-Gaussian noise, traditional spectrum sensing techniques optimized for Gaussian noise may experience significant performance degradation. To address this challenge, this paper employs Kendall's tau (KT) as a detector to detect the primary signal in additive Laplace noise. Unlike techniques relying on fundamental information from raw observation data, this detector utilizes ranks to reduce the impact of impulsive component, thus being robust against large valued outliers. The analytic expressions concerning the expectation and variance of KT under Laplace noise are firstly established. Performance analyses are further conducted in terms of false alarm probability and detection probability. Monte Carlo simulations not only verified the correctness of the established theoretical results, but also demonstrated the superiority of KT over other commonly used methods in terms of detection probability under Laplace noise.