Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

Autor:	Weißer, Franz, Turan, Nurettin, Semmler, Dominik, Utschick, Wolfgang
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators. Comment: Submitted to IEEE for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.16601 Zobrazit plný text záznamu View this record from Arxiv