Data-Aided MU-MIMO Channel Estimation Utilizing Gaussian Mixture Models

This work extends two previously proposed semi-blind channel estimators to a more general multi-user multiple-input-multiple-output (MU-MIMO) system. These estimators utilize data symbols in addition to pilot symbols to enhance the channel estimation quality. Based on all received signals, a subspace is calculated, which enhances the Gaussian mixture model based channel estimator. To estimate this subspace, we consider the inherent additional degrees of freedom in terms of precoding in MU-MIMO systems. Numerical simulations for different scenarios show that the extended methods outperform the studied state-of-the-art channel estimators.