计算机科学与探索2011，Vol.38Issue(10)：87-90,4.
基于粒子群算法的认知无线电参数优化及敏感度分析
Parameters Optimization and Sensitivity Analysis Based on Particle Swarm Optimization Algorithm in Cognitive Radios
摘要
Abstract
Cognitive radio can adaptively adjust its working parameters according to users' needs and changes in the environment, most of the existing cognitive engines use genetic algorithm to optimize parameters, however with the increase in the number of cognitive users, the increased chromosomes result in long convergence time of genetic algorithm, 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 optimal parameter solution at smaller evolution generation, reduce the optimization time, and meet the real-time processing requirement of cognitive radio.关键词
认知无线电/参数优化/粒子群算法/敏感度分析Key words
Cognitive radio,Parameter optimization,Particle swarm optimization,Sensitivity analysis分类
信息技术与安全科学引用本文复制引用
冯文江,李俊建,王品..基于粒子群算法的认知无线电参数优化及敏感度分析[J].计算机科学与探索,2011,38(10):87-90,4.基金项目
本文受国家自然科学基金(60872038),重庆市自然科学基金重点项目(CSTC2009BA2064)资助. （60872038）
