Compressed Channel Estimation based on Optimized Measurement Matrix
Chenhao Wang · Signal Processing · 2012
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.