An Novel Sparse Adaptive Blocked Matching Pursuit Algorithm for Spatio-Temporal Joint Channel Estimation in FDD Massive MIMO System

摘要

In frequency division duplex(FDD) Massive MIMO OFDM systems, downlink channel often has the characteristics of spatio-temporal common sparsity. To reduce pilot overhead, the paper adopts superimposed pilot design for spatiotemporal joint channel estimation by exploiting its properties. Due to more antennas and less pilots, channel estimation faces two challenges of accuracy and complexity in the system. In order to improve performance of estimation, we propose SAS-BOMP algorithm which optimizes and improves the BOMP algorithm. The algorithm can estimate consecutive symbols of multiple transmitting antennas simultaneously, and judging by adding a threshold, the estimation of both sparsity and sparse position is more accurate. Simulation results demonstrate that the algorithm has better mean square error (MSE) and bit error ratio(BER) performance than counterparts.