@inproceedings{ea45a86ff47243f4814a0b2b96b3216a,
title = "Data-Aided MU-MIMO Channel Estimation Utilizing Gaussian Mixture Models",
abstract = "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.",
keywords = "Gaussian mixture models, maximum likelihood, multi-user MIMO, semi-blind channel estimation",
author = "Franz Weisser and Dominik Semmler and Nurettin Turan and Wolfgang Utschick",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 59th Annual IEEE International Conference on Communications, ICC 2024 ; Conference date: 09-06-2024 Through 13-06-2024",
year = "2024",
doi = "10.1109/ICC51166.2024.10622811",
language = "English",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6684--6689",
editor = "Matthew Valenti and David Reed and Melissa Torres",
booktitle = "ICC 2024 - IEEE International Conference on Communications",
}