Abstract
In this paper, a new algorithm for sparse channel estimation in massive multiple-input-multiple-output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems is studied. The proposed algorithm reconstructs sparse channel using reciprocity of uplink and downlink channels and compressed sensing theory. Firstly, the basic model of multiple measurement vectors (MMV) for uplink sparse channel estimation in massive MIMO systems is analyzed. Then it is simplified as a single measurement vectors (SMV) model, which reduces the complexity of channel estimation and improves the accuracy of channel reconstruction. Finally, the proposed reducing MMV-based orthogonal matching pursuit (OMP) algorithm is compared with the block optimized orthogonal matching pursuit (BOOMP) algorithm. The simulation results show that the proposed algorithm can significantly improve the channel estimation performance.
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