Binary Jaya‐based channel allocation in cognitive radio networks

Abstract

Summary Cognitive radio is a key solution for wireless networks, as it offers the possibility to cope with spectrum scarcity problem. In this work, we are interested in the channel allocation problem (CA) in cognitive radio networks which is a well‐known NP‐hard optimization problem. CA has been resolved using classic evolutionary approaches such as genetic algorithms (GAs) and particle swarm optimization (PSO). However, the performance of these latters depends strongly on the parameters tuning which is a hard time‐consuming task. Under this umbrella, in this article, we propose a new solution based on Jaya algorithm which is a recent free parameters approach. The performance of our proposed algorithm is compared against GA and PSO under different network topologies, and this in terms of max‐sum‐reward, max‐min‐reward, and max‐proportional‐fair. The obtained simulation results confirm the superiority of our proposed approach under all the tested scenarios.