Fuzzy Reinforcement Learning for Routing in Multi-Hop Cognitive Radio Networks

Abstract

Cognitive radio networks (CRNs) are composed of cognitive, spectrum-agile devices capable of changing their configuration on the fly, based on the spectrum assignment policy. Moreover, the CRNs technology allows sharing of licensed spectrum band in an opportunistic and non-interfering manner. Routing and the spectrum management are the challenges in these networks. To solve these problems, in this paper, a fuzzy reinforcement learning method is proposed, where a new fuzzy reinforcement learning procedure is built in each secondary user (SU). The proposed procedure learns the best routing with a guarantee that the interference at the primary receivers is below the threshold and focuses on the problem of effective routing solutions in multi-hop CRNs.