Evolutionary Schemes for Cognitive Radio Adaptation

Radio parameter adaptation in multicarrier cognitive radio system is a challenging task. The computational complexity of parameter adaptation increases with the number of carriers, power levels and constellation size. In this paper we apply different evolutionary algorithms for cognitive radio parameter adaptation. The effectiveness of the proposed algorithms is evaluated through simulations under different environmental scenarios. Simulations results reveal that swarm family of algorithms outperform genetic algorithm-based adaptation method, towards rapid convergence of multicarrier fitness functions, provide high converged fitness values and can trade off multiple objectives more efficiently.