双蝙蝠群智能优化的多模盲均衡算法

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。

Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization

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 algorithm 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.