Bayesian Compressed Sensing-Based Channel Estimation For Massive Mimo Systems

The efficient and highly accurate channel state information (CSI) at the base station is essential to achieve the potential benefits of massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, due to limitations of the pilot contamination problem. It has recently been shown that compressed sensing (CS) techniques can address the pilot contamination problem, however, the CSbased channel estimation requires prior knowledge of channel sparsity. To solve this problem, in this paper, an efficient channel estimation approach based on Bayesian compressed sensing (BCS) that based on prior knowledge of statistical information about the channel sparsity is therefore proposed for the uplink of multi-user massive MIMO systems. Simulation results show that the proposed method can reconstruct the original channel coefficient effectively when compared to conventional based channel estimation.