Cooperative spectrum sensing based on the rao test in non-Gaussian noise environments

One of the key challenges in cognitive radio (CR) networks is to perform spectrum sensing in environments characterized by shadowing and fading effects as well as non-Gaussian noise distributions. Existing literature on spectrum sensing focuses mainly on the Gaussian noise model assumption, which does not properly characterize all the various noise types found in practical CR systems. This paper addresses the problem of spectrum sensing in the presence of non-Gaussian noise and interference for cognitive radio systems. A novel detector based on the Rao test is proposed for the detection of a primary user in the non-Gaussian noise environments described by the generalized Gaussian distribution (GGD). The test statistic of the proposed Rao detector is derived and its detection performance is analyzed and compared to that of the traditional energy detection. The Rao-based detection is then extended to a multi-user cooperative framework based on an improved decision fusion rule. It is shown through computer simulations that for a given probability of false alarm, the Rao detector can significantly enhance the spectrum sensing performance over conventional energy detection in non-Gaussian noise. Furthermore, the proposed cooperative detection scheme has a significantly higher global probability of detection than the non-cooperative scheme.