Subspace-Based Semi-Blind Channel Estimation for Large-Scale Multi-Cell Multiuser MIMO Systems

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems have received much attention recently. But the performance of these systems is deteriorated by imperfect channel state information (CSI). Hence, in this paper, a subspace-based semi-blind channel estimator is proposed. Based on the approximate orthogonality of the channel vectors of LS-MIMO systems, singular value decomposition (SVD) is employed on the received signals to determine the channel matrix up to an ambiguity matrix. Then matrix inversion is avoided in resolving the ambiguity matrix, which is essential to traditional subspace-based estimators and loses the partial CSI. The properties of the estimators are analyzed, and the analysis shows that the estimation accuracy of the proposed estimator is improved. Simulations comparing the proposed approach with others illustrate improvement of the performance of the proposed approach.