An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks

Abstract

This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.