Rao test based cooperative spectrum sensing for cognitive radios in non-Gaussian noise

Abstract

In this paper, we address the problem of spectrum sensing in the presence of non-Gaussian noise for cognitive radio networks. A novel Rao test based detector, which does not require any a priori knowledge about the primary user (PU) signal and channels, is proposed for the detection of a primary user in non-Gaussian noises that are molded by the generalized Gaussian distribution (GGD). The statistic of the proposed Rao detector is derived and its detection performance is analyzed in the low signal-to-noise ratio regime and compared to that of the traditional energy detection. Furthermore, the Rao-based detection is extended to a multi-user cooperative framework by using the “k-out-of-M” decision fusion rule and considering erroneous reporting channels between the secondary users and the fusion center due to Rayleigh fading. The global cooperative detection and false alarm probabilities are derived based on the cooperative sensing scheme. Analytical and computer simulation results show that for a given probability of false alarm, the Rao detector can significantly enhance the spectrum sensing performance over the conventional energy detection and the polarity-coincidence-array (PCA) method in non-Gaussian noises. Furthermore, the proposed cooperative detection scheme has a significantly higher global probability of detection than the non-cooperative scheme.