Efficient High-Resolution Sparse Channel Estimation Based on Temporal Correlation in MIMO-OFDM Systems

In this work, high-resolution sparse channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is addressed. Firstly, a block-structured compressed channel sensing (CCS) model with high spectral efficiency and high delay resolution is constructed. Then, by fully exploiting the temporal correlation and joint sparsity of the channels, a novel two-stage prior delay support-aided delay tracking and block residual norm minimization (PDSA-DT-BRNM) algorithm is proposed. In the first stage, with a limited number of pilots for each antenna and the delay grids within the prior delay support, an efficient delay tracking and block norm minimization algorithm is put forward to choose the common delay grids and estimate each block gain iteratively. In the second stage, by comprehensively utilizing the intermediate channel estimation results of the first stage and the prior delay support, an optimized channel estimation strategy is developed based on the block residual norm minimization (BRNM) criterion. Simulation results and theoretical analysis show the effectiveness of the proposed channel estimation scheme in terms of channel estimation performance, spectral efficiency and computational complexity.