Abstract
The channel matrix of massive MIMO-OFDM systems is sparse in the delay domain. The channel estimation based on compressed sensing uses the sparsity of the channel matrix to improve the channel estimation accuracy and reduce the pilot overhead. A channel estimation algorithm based on block compressive sampling match pursuit (B-CoSaMP) is proposed. The proposed algorithm combines the spatial correlation of the MIMO channel caused by multi-antenna propagation with the close antenna spacing at the base station, and generates an equivalent channel impulse response based on block sparsity to improve the channel estimation performance. The simulation results show that the proposed algorithm has low bit error rate and small normalized mean square error (NMSE), which is suitable for channel estimation of massive MIMO-OFDM systems.
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