Design of cognitive radio engine using artificial bee colony algorithm

Abstract

A cognitive radio engine adapts its radio parameters using metaheauristic learning algorithms in order to satisfy certain objectives in a radio environment. In this study, three evolutionary algorithms are used for optimizing the predefined fitness functions in the time varying wireless environment. The performances of genetic algorithm, particle swarm optimization and artificial bee colony algorithm are analysed in different modes of operation and in presence of spectral interference. The simulation results are compared using convergence characteristics and two statistical metrics.