Low Complexity Orthogonal Matching Pursuit-Based Near-Field Channel Estimation in XL-MIMO Systems

In the extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, the compressed sensing (CS) techniques incur prohibitive computational complexity, especially when the transmitter and receiver are both equipped with extremely large number of antennas. In this letter, we propose a multidimensional orthogonal matching pursuit relying on parameter refinement (MOMP-PR) algorithm to reconstruct the near-field channel with lower complexity. Specifically, we convert the channel estimation problem into the multidimensional CS problem where the channel is represented by a set of independent polar-domain dictionaries. Then we perform the matching projection and update the residual observation using the MOMP part to obtain the coarse estimation of channel parameters. Subsequently, these parameters can be refined to reduce the estimation error incurred by the basis mismatch between the continuous parameters in reality and on-grid parameters in the dictionary. Finally, simulation results demonstrate the effectiveness of our proposed algorithm.