基于最优观测矩阵的压缩信道感知

Compressed Channel Estimation based on Optimized Measurement Matrix

信道估计技术作为获得信道衰落信息的方法,是提高无线信道传输接收性能的关键技术。而物理多径信道固有的稀疏性,使得将压缩感知(CS)理论用于稀疏多径信道的估计成为可能。相比于传统的线性估计方法,压缩信道估计的优势在于考虑了信道的固有稀疏性,在训练序列数目较少的情况下,重构效果要明显优于传统的最小二乘估计方法;从另一个角度来说,在获得同样估计性能的情况下,压缩信道估计需要的训练序列长度也大大减少,提高了频谱资源利用率。本文在应用CS理论进行稀疏信道估计的过程中引入了最优观测矩阵,通过进一步减小随机观测矩阵的列向量相关性,使得信道估计的性能得到了进一步的改善。

Channel estimation which can acquire the channel fading information is a key technology to improve the performance at the receive node in wireless channel transmission.The inherent sparse feature of multi-path channel makes the CS theory（compressed sensing） for sparse multi-path channel estimation become possible.Compared to the traditional linear estimation method,the compressed sensing-based channel estimation method has taken the inherent sparseness of wireless channel into account.So in the case of short training sequence,the reconstruction of compressed sensing for channel estimation has a much better result than that with the traditional method of least square estimation.In another word,the length of training sequence needed in compressed channel estimation is shorter to gain the same estimation performance as the traditional one,which means the improving utilization of spectrum resources.This paper introduces a method to optimize the measurement matrix in the CS theory,and apply it to the compressed channel estimation.By reducing the correlation between the column vectors of the measurement matrix,it can lead a further improved performance in compressed channel estimation.