Bio-inspired Approaches for OFDM Based Cognitive Radio

Link adaptation algorithms design for OFDM-based cognitive radio networks is a challenging task. The main concern is to provide a high quality of service for the secondary user while the mutual interference between this last and the primary user persists within a tolerable range. This issue can be formulated as a multiobjective optimisation constraint problem. To tackle this optimisation problem in a multiobjective constraint framework, in this paper we exploit three of the most recent powerful bio-inspired algorithms: firefly, bat, and cuckoo search. Simulation results revealed that, in contrast to the classical genetic algorithm and particle swarm optimisation-based link adaptation, our proposed algorithms exhibit better performance in terms of convergence speed and solution quality with saving rates reaching over 98.93% and 46.60%, respectively.