Intelligent control of cognitive radio parameter adaption: Using evolutionary multi-objective algorithm based on user preference

Cognitive radio (CR) is a promising technique for overcoming the spectrum scarcity problem. It must appropriately alter its transmission parameters according to predefined objectives in dynamic wireless environment. In this paper, we model the CR parameter adaptation problem as an unconstrained multi-objective optimization problem and then propose a non-dominated front searching algorithm based on user preference (NFSA-UP) to determine the optimal transmission parameters for a multicarrier system. The distances from individuals to the user preference direction are combined with the pareto ranks to determine the evolving direction as well as the survive selection of individuals. It is beneficial to increase selection pressure at the beginning of the evolving, and speed up convergence to the true pareto optimal front at end. The best individual which is obtained after final iteration is reported here as the middle point on the first pareto front, avoiding the secondary selection from a set of optimal solutions. We performed multi-objective optimization on a 64 subcarriers in CR network. NFSA-UP is compared with other pareto front searching algorithms NSGAII and NSGAII-LBS, and the results demonstrate that the optimal transmission parameters of CR can be got using NFSA-UP with any user preference direction, while better performance is observed.