基于模因算法的多模盲均衡算法

由于常模盲均衡算法(Constant modulus blind equalization,CMA)收敛速度和均方误差都不甚理想，且对多模信号均衡时会发生相位旋转，本文提出了基于模因算法的多模盲均衡算法(Multi-modulus blind equalization algorithm based on memetic algorithm,MA-MMA)。该算法将多模盲均衡算法(Multi-modulus blind equalization algorithm,MMA)代价函数的倒数作为模因算法(Memetic algorithm,MA)的适应度函数，利用MA全局优化机制和局部深度搜索能力，在每次全局搜索后对全部新产生的个体进行局部深度搜索，将全局和局部搜索得到的最优个体解向量作为MMA的初始最优权向量。仿真结果表明，与传统的CMA,MMA以及基于遗传算法的多模盲均衡算法相比，MA-MMA 的收敛速度最快,稳态误差最小,输出信号星座图最清晰。

Multi-modulus Blind Equalization Algorithm Based on Memetic Algorithm

Due to the slow convergence speed, large mean square error(MSE) and existing blind phase for the constant modulus blind equalization algorithm(CMA), a multi-modulus blind equalization algorithm based on memetic algorithm (MA-MMA) is proposed. In this algorithm, the reciprocal of the cost function of multi-modulus blind equalization algorithm(MMA) is defined as the fitness function of the memetic algorithm (MA). The solution vector of individual in the whole group is regarded as the initial weight vector of MMA. The vector of the individual in whole groups corresponding to the fitness function maximum is searched by the global information sharing mechanism and local depth search ability of MA and used as the initial optimum weight vector of MMA. After the weight vector of MMA is updated, the optimal weight vector of MMA is obtained. Simulation results prove that compared with CMA, MMA. The multi-modulus blind equalization algorithm based on genetic algorithm(GA-MMA) which has recently been proposed, the proposed MA-MMA has the fastest convergence speed, the smallest MSE, and the clearest constellations of output signals.