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.
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-interference-noise-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.
作者
王宏志
姜方达
周明月
WANG Hongzhi;JIANG Fangda;ZHOU Mingyue(School of Computer Science and Engineering,Changchun University o f Technology,Changchun 130012,China)
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2018年第6期1483-1487,共5页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61501059)
吉林省教育厅科研项目(批准号:2016343)
关键词
认知无线电
功率分配
粒子群优化算法
cognitive radio
power allocation
particle swarm optimization(PSO)algorithm
作者简介
王宏志(1961—),男,汉族,博士,教授,博士生导师,从事数字信号处理与应用及图像处理的研究,E-mail:wanghongzhi@ccut.edu.cn.;通信作者:周明月(1980-),女,汉族,博士,讲师,从事认知无线电系统中资源分配问题的研究,E-mail:zmyjlu@ccut.edu.cn.
