【摘要】 针对认知无线电网络(CRN)中主用户(PU)的干扰功率阈值、次用户(SU)的传输速率限制和信干噪比(SINR)需求,提出一种基于蒸发因子的粒子群优化(LTPSO)算法,其中蒸发因子根据粒子群学习因子设定,建立新的粒子群记忆形式,并对适应度值按比例进行筛选.仿真结果表明,LTPSO算法获得了较好的优化效果.更多还原

【Abstract】 Aiming at the interference power threshold of the primary user(PU)in the cognitive radio network(CRN),transmission rate limit of the secondary user(SU)and the signal-to-interferencenoise-ratio(SINR)requirement,we proposed a learning traditional particle swarm optimization(LTPSO)algorithm,in which the evaporation factor was set according to the particle swarm learning factor,a new particle swarm memory form was established,and the fitness values were screened proportionally.The simulation results show that the LTPSO algorithm achieves better optimization results.更多还原