线性均衡和判决反馈均衡LMS算法仿真分析

Simulation Analysis of Linear Equalization and Decision Feedback Equalization Based on LMS Algorithm

介绍基于自适应最小均方线性均衡和判决反馈均衡算法的原理,并通过实验仿真比较两种算法在训练判决引导混合模式下的均衡性能,分析反馈滤波器长度对判决反馈均衡器性能的影响。结果表明:在训练阶段,最小均方线性均衡算法优于最小均方判决反馈均衡算法的性能;在判决阶段,良好信道条件下最小均方线性均衡具有比较理想的性能,当信道条件恶劣时,最小均方线性均衡算法性能变差,而最小均方判决反馈均衡算法随着反馈滤波器长度增加,均衡效果更优。

Two schemes including both linear equalization（LE） and decision feedback equalization（DFE） are introduced based on the adaptive least mean square（LMS） algorithm.Simulations are utilized to compare the equalization performance of the LELMS and the DFELMS in the hybrid mode of training and decision-directed.Moreover,the influence of the length of feedback filter on the DFELMS algorithm is analyzed.Results indicate that in the training period,the LELMS outperforms the DFELMS;in the decision-directed phase,for the channel in good condition,the LELMS has a desirable equalization performance comparatively;in the case of the ill-conditioned channel,the LELMS degrades greatly whereas the DFELMS equalizes the channel better as the length of feedback filter increases.