A Channel Selection Algorithm Based on Fuzzy Logic and Learning Automata for Cognitive Radio Sensor Networks

Abstract

In this paper, we propose a channel selection scheme for secondary users in cognitive radio sensor networks, which includes learning automata and fuzzy logic system (FLS). In the proposed scheme, FLS is used as the channel selection mechanism while the learning automata algorithm is being used to learn the radio environment such as channel link quality. Signal to noise ratio of the link between primary user (PU) and secondary user (SU), the probability of choosing channel, and signal to noise ratio of the link between secondary users are chosen as input parameters for the FLS to decide one data channel among multiple channels. Simulation results show that the proposed scheme does indeed provide advantages in improving the throughput of CR networks, in comparison with some other previous schemes.