基于差分进化的时变信道最大似然估计算法

研究了隧道环境下的通信信道估计。针对隧道环境的地铁列车与轨旁设备之间无线通信中无线传输信道快速变化的特点,提出了一种采用元胞差分进化(DE)方法实时获取时变信道的有效信道长度的新型最大似然(ML)信道估计算法——DE-ML算法。仿真结果表明该算法在使用较少导频信息的情况下,通过差分进化方法有效估计跟踪有效信道长度,其估计性能优于最小二乘(LS)、线性最小均方误差(LMMSE)、传统ML等经典信道估计算法。该算法能在提高系统传输效率的同时显著提高算法的估计精度,尤其在高速移动情况下也具有了非常良好的性能。

An algorithm for maximum-likelihood estimation of time-varying channels based on differential evolution

The estimation of the communication channels in tunnel environment was studied, and a new maximum-likeli-hood ( ML) channel estimation algorithm using the cellular differential evolution ( DE) algorithm to achieve the re-al-time estimation of the effective length of time-varying channels was designed to deal with the rapid changing wire-less transmission channels under the tunnel environment.The algorithm is simplified as the DE-ML algorithm.The simulation results show that the proposed algorithm can effectively track the effective channel length to improve the estimation accuracy by the differential evolution algorithm.And its estimation performance outperforms the classic channel estimation algorithms such as least square ( LS) , linear minimum mean square error ( LMMSE) and tradi-tional ML, etc.The proposed algorithm can offer high transmission efficiency and excellent estimation performance especially at time-varying channels.