A Novel Cognitive Radio Adaptation for Wireless Multicarrier Systems

This paper deals with the transmission parameter adaptation problem in a dynamic wireless channel environment for multicarrier-based cognitive radio systems. Given the environmental parameters returned by sensors, the cognitive radio will select a set of transmission parameters that can best respond to the new conditions. However, due to many possible values for the transmission parameters, the adaptation of radio parameters to generate optimum transmitted signals according to the changing environment and user needs is rather complex, especially for the multicarrier system with a large number of subcarriers. Inspired by the efficient ability of the cross-entropy (CE) method to find near-optimal solutions in huge search spaces, the application of the CE method to optimize cognitive radio parameters given a set of objectives is proposed. Computer simulation results show that the proposed CE method has significantly faster convergence than the conventional particle swarm optimization (PSO) method. Moreover, the parameters optimized by the proposed CE method have higher fitness values than those optimized by PSO.