Cooperative Q-learning based channel selection for cognitive radio networks

This paper deals with the jamming attack which may hinder the cognitive radio from efficiently exploiting the spectrum. We model the problem of channel selection as a Markov decision process. We propose a real-time reinforcement learning algorithm based on Q-learning to pro-actively avoid jammed channels. The proposed algorithm is based on wideband spectrum sensing and a greedy policy to learn an efficient real-time strategy. The learning approach is enhanced through cooperation with the receiving CR node based on its sensing results. The algorithm is evaluated through simulations and real measurements with software defined radio equipment. Both simulations and radio measurements reveal that the presented solution achieves a higher packet success rate compared to the classical fixed channel selection and best channel selection without learning. Results are given for various scenarios and diverse jamming strategies.