Tensor-Based Orthogonal Matching Pursuit with Phase Rotation for Channel Estimation In Hybrid Beamforming Mimo-Ofdm Systems

Abstract

Tensor decomposition is often employed for channel estimation in hybrid beamforming MIMO-OFDM systems because of multiple dimensions and channel sparsity. We propose to incorporate phase rotation in factor matrices of tensor-based orthogonal matching pursuit (T-OMP) algorithm to solve the energy leakage problem caused by the grid constraint. The phase rotation can be applied in all the dimensions of virtual channel tensor including angle of arrival (AoA), angle of departure (AoD), and delay for grid refinement. Consequently, fewer iterations are required to estimate the sparse coefficients in the core tensor. In addition, the tensor fusion technique is also proposed to further improve the performance. With the grid refinement, the number of required coefficients in the core tensor is reduced and close to the number of paths. Hence, compared to the conventional T-OMP algorithms, less computation complexity is needed while better performance can be achieved.