Channel Estimation Based on Compressive Sensing for Multi-User Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is becoming a key technology for future wireless communications. Channel feedback for massive MIMO is a challenging task due to the increased dimension of MIMO channel matrix. By exploiting the channel sparsity, channel estimation based on compressive sensing (CS) aims to reduce the feedback overhead in massive MIMO systems. In this paper, various CS algorithms for channel estimation in massive MIMO systems are summarized, and a novel CS algorithm, i.e. modified sparsity adaptive matching pursuit (MSAMP), is proposed hereupon. Moreover, various measurement matrices are introduced in the CS scheme. The performances of channel estimation and recovery are simulated and compared. It is inferred from simulation that, SAMP is very appropriate for reconstructing sparse channel information in massive MIMO system, especially the modified SAMP can give higher reconstruction, and some measurement matrix is suitable for certain optimal CS algorithms.