Cooperative learning for reduced complexity cross-layer cognitive radio

A Cognitive Radio (CR) network has to adapt operations of all secondary users to meet performance goals while avoiding interfering the primary network (PN) beyond a set limit. For this, this paper considers a distributed cross-layer resource allocation CR algorithm. While the cross-layer approach notably improves performance in terms of average end-to-end distortion and network's congestion rate, it increases the number of iterations needed to find the resource allocation solution. In this paper, the extra complexity in the cross-layer approach is addressed through a novel cooperative cross-layer learning algorithm where peer nodes cooperate first distributing the learning tasks, followed by sharing of the complementary learned information. The algorithm does not rely on the availability of expert nodes that have already performed the learning process. Simulation results show that the cooperative learning technique reduces complexity by approximately 45% with a small and very acceptable sacrifice in performance.