Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

TL;DR

This paper introduces two semi-blind channel estimation methods that leverage data symbols and Gaussian mixture models to enhance accuracy in multi-user systems, outperforming existing estimators.

Contribution

The paper presents novel semi-blind channel estimation techniques that incorporate data symbols and subspace estimation to improve Gaussian mixture model-based estimation.

Findings

Proposed methods outperform state-of-the-art estimators in simulations.

Methods enable parallelization and precomputation for efficiency.

Numerical results confirm improved estimation accuracy.

Abstract

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.