基于粒子群算法的认知无线电参数优化及敏感度分析

认知无线电能根据环境变化和用户需求自适应调整工作参数。现有认知引擎大多采用遗传算法优化参数。但随着认知用户数的增加，遗传算法染色体增多，导致算法收敛时间过长，无法满足实时通信需求。将改进惯性因子的粒子群算法用于认知无线电工作参数的优化，并在不同通信模式下对传输参数进行敏感度分析，以便有选择性地从目标函数中剔除敏感度较低的参数，降低处理复杂度。仿真结果表明，采用粒子群算法的参数优化在收敛速度、搜索效率和算法稳定性等方面均优于遗传算法，仅需较小的进化代数就能找到最优参数解，从而减小了优化时间，满足了认知无线电实时处理的要求。

Parameters Optimization and Sensitivity Analysis Based on Particle Swarm Optimization Algorithm in Cognitive Radios

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.