Sigmoid function detector in the Presence of heavy-tailed noise for multiple antenna cognitive radio networks

Abstract

An efficient spectrum sensing scheme is proposed for detecting a primary signal corrupted by a Generalized Gaussian Noise (GGN). It is assumed that the power of the primary signal is much smaller than the noise power, and secondary users are equipped with multiple antenna receivers. The proposed scheme, referred to as sigmoid detector, uses a sigmoid soft limiter. The sigmoid limiter is characterized by a parameter which controls the softness of its limiter. The sigmoid parameter is optimized to achieve the best performance(probability of detection) using the chernoff bound for probability of detection. It is shown through simulations that the sigmoid detector outperforms well known detectors such as SL-PCA (soft-limited polarity coincidence array) with soft limiting function, PCA (polarity coincidence array) and the energy detection (ED) scheme in the presence of heavy-tailed GGN. Also, there is a match between the simulation and analytical results.