Sparse Channel Estimation for Massive MIMO System Based on Dirichlet Process and Combined Message Passing

This paper investigate the problem of estimating sparse channels in massive MIMO systems. Most wireless channel are sparse with large delay spread, while some channels can be observed have common support within a certain area of the antenna array. This common support property is attractive when it comes to the estimation of large number of channels in massive MIMO systems. In this paper, we proposed a novel channel estimation approach which utilize the common support by exerting a Dirichlet process (DP) prior over the sparse Bayesian learning (SBL) model. In addition, this Dirichlet process is modeled based on factor graph and combined BP-MF message passing. Compare to the variational Bayesian (VB) method in literaturewhich, the proposed method can improve the performance while significantly reduce the complexity. Simulation results demonstrate that the proposed algorithm outperform other reported ones in both performance and complexity.