ILFCS: an intelligent learning fuzzy-based channel selection framework for cognitive radio networks

Abstract

Cognitive radio networks (CRNs) have been introduced as a promising solution to optimize the use of available radio-frequency spectrum. The key idea in CRNs is the proper selection of available sensed channels. In this paper, an intelligent distributed channel selection strategy is proposed for cognitive radio ad-hoc networks aiming to assist them in selecting the best channel for transmission. The proposed strategy classifies the available channels based on the primary users’(PUs) utilization, the number of cognitive radio neighbors using the channels, and the capacity of available channels. The Fuzzy Logic technique is used to determine a channel’s weight value by combining these parameters. The channels with the highest weight value are selected for transmission. The proposed strategy takes into account false alarm (FA) and miss detection (MD) metrics to classify the sensed channels into four categories (FA, MD, ON and OFF) based on K-means learner. This classification helps the strategy to avoid accessing occupied channels. Simulation results based on NS2 simulation approved that the proposed strategy is effective compared to other strategies concerning selecting the best channel and achieving higher channel utilization.