CAJ | 학술논문

针对常模盲均衡算法( CMA)均衡多模QAM信号收敛速度慢、剩余均方误差大的缺陷,提出了一种基于双蝙蝠群智能优化的多模盲均衡算法( DBSIO-MMA). 该算法将2个蝙蝠群独立全局寻优得到的一组最优位置向量分别作为多模盲均衡算法( MMA)初始化最优权向量的实部与虚部,以此提高收敛速度并减小剩余均方误差. 仿真结果表明,蝙蝠算法( BA)全局搜索成功率高、收敛速度快的特点在DBSIO-MMA中得到很好地体现. 与CMA、MMA、粒子群多模盲均衡算法( PSO-MMA)、单蝙蝠群多模盲均衡算法( BA-MMA)相比,DBSIO-MMA具有更快的收敛速度和更小的均方误差.
침대상모맹균형산법( CMA)균형다모QAM신호수렴속도만、잉여균방오차대적결함,제출료일충기우쌍편복군지능우화적다모맹균형산법( DBSIO-MMA). 해산법장2개편복군독립전국심우득도적일조최우위치향량분별작위다모맹균형산법( MMA)초시화최우권향량적실부여허부,이차제고수렴속도병감소잉여균방오차. 방진결과표명,편복산법( BA)전국수색성공솔고、수렴속도쾌적특점재DBSIO-MMA중득도흔호지체현. 여CMA、MMA、입자군다모맹균형산법( PSO-MMA)、단편복군다모맹균형산법( BA-MMA)상비,DBSIO-MMA구유경쾌적수렴속도화경소적균방오차.
Aiming at the defects of the large surplus mean square error and slow convergence speed in equalizing multi-modulus QAM signals by utilizing constant modulus algorithm ( CMA) , a multi-modulus blind equalization al-gorithm based on double bat swarms intelligent optimization ( DBSIO-MMA) is proposed. In the algorithm, a group of optimal position vectors attained by independent global optimization of two bat swarms are respectively taken as the real and imaginary parts of the initialized optimal weight vector, so as to improve convergence speed and reduce surplus mean square error. The simulation results show that the features of fast convergence speed and high success rate of the bat algorithm ( BA) in global search are fully reflected in the proposed algorithm. Compared with the CMA, multi-modulus blind equalization algorithm ( MMA) , particle swarm optimization based MMA ( PSO-MMA) and bat swarms intelligent optimization based MMA ( BA-MMA ) , the proposed algorithm has faster convergence speed and smaller mean square error.