Enhance Channel Selection in Cognitive Radio Networks based on Fuzzy Logic

The great increase in devices (laptops, smartphones, etc.) during the last decade that uses wireless communication has increased the need for different spectral frequencies, for their use by these devices. This demand has led to a phenomenon called spectrum scarcity. Statistics issued by the Federal Communications Commission (FCC) have shown inefficiency in allocating fixed spectrum. To solve this problem (spectrum scarcity) the concept of cognitive radio is proposed. One of the promising responses to use the available (unused) spectrum is the so-called cognitive radio (CR), which was developed to enable secondary users (SUs) to exploit the spectrum holes in authorized channels. Secondary users should quickly clear the channel and change to another available channel if access to the primary user is sensed.This paper proposes the use of artificial intelligence techniques to improve the selection of the best available channels. The metrics to evaluate the artificial intelligence techniques are channel switching, throughput, and delay time.The simulations were done using MATLAB Simulator. Neural network techniques have shown promising results compared to some FIS (TSK, Mamdani).