Parameters Optimization and Sensitivity Analysis Based on Particle Swarm Optimization Algorithm in Cognitive Radios
基于粒子群算法的认知无线电参数优化及敏感度分析

Cognitive radio can adaptively adjust its working parameters according to users' needs and changes in the en- vironment, most of the existing cognitive engines use genetic algorithm to optimize parameters, however with the in- crease in the number of cognitive users, the increased chromosomes result in long convergence time of genetic algo- rithm,which can't meet the needs of real-time communication. An improved inertia factor particle swarm optimization was used for parameter optimization in cognitive radio, and parameter sensitivity analysis on transmission parameters was done in different communication modes,so as to remove lower sensitivity parameters selectively from the objective function, and reduce the processing complexity. Simulation results show that parameter optimization based on particle swarm optimization has better convergence,efficiency and stability than genetic algorithm,and can successfully find op- timal parameter solution at smaller evolution generation, reduce the optimization time, and meet the real-time processing recauirement of cognitive radio.