基于量子粒子群优化的正交小波加权多模盲均衡算法

针对常数模盲均衡算法(CMA)均衡高阶正交振幅调制信号(QAM)存在收敛速度慢、稳态误差大的缺点, 提出了基于量子粒子群优化的正交小波加权多模盲均衡算法(QPSO-WTWMMA). 该算法根据高阶QAM信号星座图分布特点, 将量子粒子群优化算法(QPSO) 和正交小波变换融入于加权多模盲均衡算法(WMMA)中. 因而, 利用QPSO对均衡器权向量进行了优化, 利用正交小波变换降低了输入信号的自相关性, 利用WMMA选择了合适的误差模型匹配QAM星座图. 理论分析及水声信道仿真结果表明, QPSO-WTWMMA算法可以获得更快的收敛速度和更低的稳态误差, 在水声通信中具有重要的参考价值.

Orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on quantum particle swarm optimization

When constant modulus blind equalization algorithm (CMA) is used to equalize high-order QAM signals, there occur the defects of the slow convergence rate and big steady mean square error. In order to overcome these disadvantages, orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on the quantum particle swarm optimization (QPSO-WTWMMA) is proposed. In this proposed algorithm, quantum particle swarm optimization algorithm and orthogonal wavelet transform are combined into weighted multi-modulus blind equalization algorithm (WMMA) according to the feature of higher-order QAM signal constellations. Accordingly, the equalizer weight vector can be optimized by QPSO algorithm, the autocorrelation of the input signals can be reduced via using orthogonal wavelet transform, and WMMA is used to choose appropriate error models to match QAM constellations. The theoretical analyses and the computer simulations in underwater acoustic channels indicate that the proposed algorithm can obtain the fastest convergence rate and the smallest steady mean square error in equalizing high-order QAM signals. So, the proposed algorithm has important reference value for the underwater acoustic communications.