Compressive Channel Estimation Exploiting Block Sparsity in Multi-User Massive MIMO Systems

Abstract

Massive multiple input multiple output (MIMO) is a promising technology that can enhance the wireless communication capacity due to increased degrees of freedom. To fully utilize the spatial multiplexing gains of massive MIMO, accurate channel state information (CSI) is required for coherent detection. Due to the overwhelming pilot overhead of conventional CSI estimation methods, compressed sensing technology is adopted as an effective method to reduce pilot overhead. In this paper, we consider the channel estimation problem in FDD multi-user massive MIMO systems. By exploiting the block sparsity of channel matrices in virtual angular domain among different users, we propose a joint block orthogonal matching pursuit (JBOMP) algorithm to estimate CSI at the base station. The performance of JBOMP is evaluated by simulation, which shows the advantages over existing algorithms