基于混沌萤火虫优化的小波多模盲均衡算法

Wavelet Multi-modulus Blind Equalization Algorithm Based on Chaos Glowworm Optimization

采用多模盲均衡算法(MMA)处理高阶正交振幅调制QAM信号时,存在收敛速度慢、稳态误差大、容易陷入局部最优等问题。为此,提出一种基于混沌萤火虫优化的正交小波多模盲均衡算法(CGSO-WT-MMA)。该算法将具有良好全局搜索能力的萤火虫算法和具有较强局部搜索能力的混沌算法相结合,用以优化均衡器权向量,并引入正交小波变换降低信号自相关性,以改善收敛性能。仿真实验结果表明,与MMA算法相比,该算法均方误差降低近4 dB,收敛速度加快近5 000步,稳态性能明显提高。

Multi-Modulus Algorithm（MMA） used to equalize high-order Quadrature Amplitude Modulation（QAM） has many disadvantages, such as slow convergence rate, large mean square error, and easily immerging in partial minimum. In order to overcome the problems, orthogonal Wavelet Transform Multi-modulus blind Equalization Algorithm based on Optimization of Chaos Glowworm Swarm Optimization（CGSO-WT-MMA） is proposed. In the proposed algorithm, MMA is integrated with CGSO and WT, the de-correlation ability of WT is used to reduce the signal autocorrelation, and the global search ability of GSO algorithm integrating with the local search ability of chaos algorithm is used to optimize the equalizer weight vector. Simulation experimental results show that compared with MMA algorithm, mean square error of the algorithm decreases 4 dB, convergence rate speeds up 5 000 step, and its steady state performance has obvious imorovement.