Compressed Channel Estimation based on Optimized Measurement Matrix

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 multipath channel makes the CS theory (compressed sensing) for sparse multipath channel estimation become possible. Traditional linear estimation method does not take the inherent sparse feature of the channel into account, So the reconstruction of compressed sensing for channel estimation has a much better result than that with the traditional method of least square estimation when the training sequence is short. To get the same estimation performance, compressed sensing-based channel estimation methods need much less training sequences, improving utilization of spectrum resources, which proves the excellent performance of compressed channel estimation. In this paper, when applying the compressed sensing theory to sparse channel estimation, by reducing the correlation between column vectors of the measurement matrix to form a optimized measurement matrix, it can lead a further improved performance in sparse channel estimation.