Sparsity Adaptive Channel Estimation Algorithm Based on Compressed Sensing

Abstract

Aiming at the problem of path selection error and the prior information of channel sparsity in compressed sampling matching pursuit algorithm, an improved sparsity adaptive compressed sensing algorithm based on atomic screening is proposed. The improved algorithm uses the time-domain screening characteristics of traditional DFT channel estimation algorithm to screen out most noise atoms, then uses the atom set obtained as priori information to reduce the probability of error selection in traditional compressed sensing algorithm. In addition, the idea of step size is introduced. The number of atoms entering the candidate set in each iteration is set to twice the step size, further reducing the possibility of irrelevant atoms entering the candidate set. Finally, the number of iterations is controlled by the change of residual, which improves the adaptability of channel estimation algorithm. The simulation results show that the mean square error performance of the improved algorithm in the channel with unknown sparsity is 4dB higher than that of the original algorithm.