基于混合遗传优化的正交小波变换盲均衡算法

Hybrid Blind Equalization Algorithm Based on Genetic Algorithm and Wavelet Transform

常数模算法(CMA)收敛速度慢,初始权向量的确定缺乏理论依据,容易陷入局部极小值。针对这些问题,在正交小波变换盲均衡算法(WT-CMA)的基础上,提出了基于混合遗传优化的正交小波变换盲均衡算法(GAWT-CMA)。该算法在常规遗传算法的父代和子代之间嵌入WT-CMA形成混合算法,利用遗传算子的全局收敛性进行宏观搜索,用WT-CMA进行局部搜索。由混合算法用少量数据进行权向量的优化,在全局范围内获得较好的初始权向量,再用WT-CMA收敛到全局最优值。水声信道的仿真结果显示,GAWT-CMA完成收敛比CMA快约9 000次,比WT-CMA快约3 000次,比GA-CMA快约1 000次,稳态误差对比CMA小约3dB,比WT-CMA小约1dB,比GA-CMA约小0.5dB。

Constant modulus algorithm （CMA） has slow convergence speed and easily immerges in partial minimum owing to the lack of initialization theory. Aiming at these disadvantages, hybrid blind equalization algorithm based on genetic algorithm and wavelet transform （GAWT-CMA） is proposed to improve the performance of CMA. The proposed hybrid algo- rithm is formed by embeding WT-CMA in normal genetic algorithm. It uses the global convergence of genetic operator to conduct a macro-search, and conducts micro-search with WT- CMA. It obtains a better initialization weight vector in overall range with a little bit of data by the hybrid algorithm, and then gets globally optimal solution by WT-CMA. Computer simulation with underwater acoustic channel shows that GAWT-CMA＇s convergence is about 9 000 times faster than CMA, about 3 000 times/aster than WT-CMA, and its steady-state error is smaller than CMA by 3 dB, smaller than WT-CMA by about 1 dB.