Robust FDD M-MIMO Channel Estimation with Structured Orthogonal Matching Pursuit Algorithm

Massive MIMO(M-MIMO) is recognized as one of the promising technologies to meet the demands of the fifth generation of wireless telecommunications. The channel matrix of OFDM Massive MIMO systems is sparse in delay domain. In the compressed sensing-based channel estimation, the channel matrix sparsity is used to improve the channel estimation accuracy and decrease the pilot overhead. This study presents a structured compressed sensing channel estimation plan to reduce the required pilot. As a result, it is possible to enhance the inherent spatial sparsity of the M-MIMO delay domain channels. We propose an algorithm that estimates the channel based on the greedy orthogonal matching pursuit (OMP) algorithm. This algorithm uses the common spatial sparsity of M-MIMO channels for accurate channel estimation. We also present simulations that show the capacity of the proposed approach for reducing the required pilot. The simulation results indicate that the presented channel estimation method has a low bit error rate. In addition, it reliably obtains the sparsity of the channel, suggesting its suitability for channel estimation in OFDM M-MIMO systems.