Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making

The design of cognitive radio engine aims at adapting the radio parameters to a predefined set of objective functions in communication system and may be formulated as a constrained multiobjective optimization problem. In the proposed work, an efficient design of orthogonal frequency division multiplexing based cognitive radio is carried out using multiobjective evolutionary algorithms. The performances of different algorithms are assessed and compared using three statistical metrics. The simulation results show that our proposed approach outperforms other algorithms while designing a cognitive radio engine. Our proposed approach which is based on the concept of cat swarm optimization, not only efficiently computes but also finds better nondominating solutions. In this paper, multiobjective evolutionary algorithms are applied to the parameter adaptation of a OFDM based cognitive radio engine. The spectral interference between primary and cognitive users is taken into consideration which plays a major role in communication. Due to heuristic nature of evolutionary algorithms, the stability of the simulation results is verified using different statistical tests. A fuzzy logic based strategy is shown in order to find out a compromised solution on the Pareto front.