Massive MIMO-OFDM Channel Estimation via Structured Turbo Compressed Sensing

In this paper, we consider the design of efficient channel estimation algorithms for downlink massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. By exploiting the channel sparsity, compressed sensing can be used to reduce the pilot overhead in channel identification. Among various compressed sensing algorithms, turbo compressed sensing (Turbo-CS) provides a generic framework for sparse signal recovery with low computational complexity and good performance. In this paper, we propose a structured Turbo-CS (STCS) algorithm to efficiently handle the sparsity of the MIMO-OFDM channel in various transform domains, including the angular domain, the frequency domain, as well as the delay domain. We show that the performance of the proposed algorithm can be characterized by state evolution. We also show that the proposed algorithm can achieve considerable performance gain, as compared with its counterparts.