Factor Graph Based Cooperative Spectrum Sensing in Cognitive Radio over Time-Varying Channels

Abstract

A normal factor graph (NFG) based approach for cooperative spectrum sensing in cognitive radio over time varying and frequency non-selective fading channels is presented in this paper. An NFG based representation of a distributed cognitive radio system is first presented and then a Sum-Product- Algorithm (SPA) based analysis is developed for inference. The spectrum sensing problem is modelled as a distributed binary hypothesis testing problem. A Neyman-Pearson (NP) based likelihood ratio test statistic is derived for optimal sensing. As exact theoretical analysis of the system level probability of detection and probability of false alarm is very difficult, we provide an approximation which performs satisfactorily in the moderate to high signal-to-noise ratio (SNR) regime. The proposed NFG based spectrum sensing approach is computationally scalable to large networks and performs well under time varying channel conditions. Extensive simulation results are provided to validate our proposed approximation.