Distributed average consensus optimization for cooperative spectrum sensing in cognitive radio ad hoc networks

Abstract

SummaryIn this article, we develop a parameter and optimization analyses aiming at characterizing the performance of cooperative spectrum sensing (CSS) based on the distributed average consensus (DAC), taking into account the network topology effect, represented by adjacency matrices in cognitive radio ad hoc networks. The CSS performance is analyzed by the probability of detection, probability of false alarm, and probability of miss detection, besides the decision threshold and the number of collected samples. In this work, all secondary users are equipped with a simple energy detector. Despite the DAC technique has been successfully deployed in the distributed CSS context, however, a full analytical description taking into account the joint effects of the network topology and all the optimized system parameters remains unknown. Moreover, the CSS network decision threshold was optimized using the error probability minimization criterion. Numerical results deploying extensive Monte Carlo simulations have corroborated the proposed analytical expressions.