Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
Abstract
In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy and sparsely allocated pilot observations. In a practical application, recent pilot observations at the base station (BS) can be utilized for training. This is in sharp contrast to state-of-theart machine learning (ML) techniques where a training dataset consisting of perfect channel state information (CSI) samples is a prerequisite, which is generally unaffordable. In particular, we propose an adapted training procedure for fitting the GMM which is a generative model that represents the distribution of all potential channels associated with a specific BS cell. To this end, the necessary modifications of the underlying expectation-maximization (EM) algorithm are derived. Numerical results show that the proposed estimator performs close to the case where perfect CSI is available for the training and exhibits a higher robustness against imperfections in the training data as compared to state-of-the-art ML techniques.
Cite
@article{arxiv.2301.06488,
title = {Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation},
author = {Benedikt Fesl and Nurettin Turan and Michael Joham and Wolfgang Utschick},
journal= {arXiv preprint arXiv:2301.06488},
year = {2023}
}