Hierarchical Sparse Estimation of Non-Stationary Channel for Uplink Massive MIMO Systems

This paper proposes a hierarchical sparse estimation of spatial non-stationarity channel for uplink massive multiple-input multiple-output (MIMO) systems without prior information. Especially, the non-zero rows of non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. A row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer of the proposed algorithms, and multiple non-zero rows can be estimated in one iteration, which has higher precision and lower complexity, compared to the conventional SAMP. Different from the existing two-layer iteration algorithms, a threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. Further, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of channel coefficient estimation, and computational efficiency.