基于隐训练序列的信道估计与跟踪

提出了新的基于隐训练序列的频率选择性信道估计方法,利用训练序列与信息序列的不相关特性,在没有带宽损失的情况下估计出信道参数。文中对所提方法给予了证明,给出了信道估计算法,并提出了改进的自适应形式,可以用于跟踪时变信道。与以往的隐训练序列估计方法比较,文章中的算法具有更低的估计均方误差,不受接收端直流偏移的限制,且适用于时变信道。计算机仿真结果表明了该估计方法的有效性。

Channel estimation and tracking using implicit training

A new estimation method based on the superimposed (implicit) training was proposed for the frequency selective channel. By exploiting the uncorrelation between the training and the information sequences, accurate channel parameters could be obtained without loss of bandwidth. We proved our method and derive a close form solution for the estimation. In addition, an improved adaptive algorithm was presented to track the time-varying channel. Compared with the existing approaches, our method was able to achieve lower MSE, unaffected by unknown dc offset at the receiver, and applicable to time-varying channel tracking. The simulation results show the effectiveness of the proposed method.