Using belief propagation to counter correlated reports in cooperative spectrum sensing

Consider spectrum sensing systems where the presence of the primary user of the spectrum is detected by secondary users (SUs) in a centralized cooperative fashion. The sensing results can be correlated due to environmental reasons. SUs may or may not be honest. If dishonest, they could be colluding. Existing methods to fuse the SU reports either ignore the correlation in the SU reports, or they need to know the source of correlation. In this paper, we propose a belief propagation based fusion algorithm to exploit the correlations in reports of groups of SUs irrespective of cause. We show that identifying the groups of SUs having correlated reports reduces the probability of error of spectrum sensing. Our method is based on modeling the probability distribution underlying the SU reports as a Bayesian network. The process of learning the Bayesian network also shows that it is theoretically impossible to identify collusion.