Joint Spectrum Sensing and Jamming Detection with Correlated Channels in Cognitive Radio Networks

In a cognitive radio (CR) scenario, we study the joint problem of spectrum sensing and jamming detection. Modelling the scenario as a multiple hypothesis testing problem, we analyse the probability of detection of the optimal detector in the sense of Neyman-Pearson theorem. We derive one exact form in terms of a series and a closed-form version. Moreover, we evaluate the asymptotic probability of detection, as it results in a simpler form to handle. In all of the above analysis, we consider the spatially correlated observation data. We further consider two practical scenarios where first we have no knowledge of the jammer's signal, and second where we have no knowledge of the noise power. We apply generalized likelihood ratio test in both of the cases. Simulation results confirm the accuracy of our asymptotic performance derivations.