基于粒子群优化的正交小波盲均衡算法

An Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Particle Swarm

为克服常数模算法(CMA)收敛速度慢、稳态误差大的缺点,在分析正交小波常数模盲均衡算法(WT-CMA)基础上,该文提出了基于粒子群优化的正交小波常模盲均衡算法(PSO-WT-CMA)。该算法利用粒子群的信息共享机制和有效的全局搜索特点,寻找最优的均衡器权值,并用正交小波变换降低信号的自相关性。水声仿真结果表明:与常数模算法(CMA)、基于粒子群优化的常数模盲均衡算法(PSO-CMA)和基于正交小波变换的常数模盲均衡算法(WT-CMA)相比,该算法在提高收敛速度和减小码间干扰方面的性能有很大的改善。

In order to overcome the slow convergence rate and big mean square error of Constant Modulus Algorithm（CMA）,an orthogonal wavelet transform constant modulus blind equalization algorithm based on the optimization of particle swarm is proposed,on the basis of analyzing the futures of orthogonal Wavelet Transform Constant Modulus blind equalization Algorithm（WT-CMA） and particle swarm algorithm.In the proposed algorithm,the equalizer weight vector can be optimized via making full use of effective global search of particle swarm algorithm and the de-correlation ability of wavelet transform.Computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms the CMA,the constant modulus blind equalization algorithm based on the Particle Swarm Optimization（PSO-CMA） and WT-CMA in improving the convergence rate and reducing inter symbol interference.