Learning a Gaussian Mixture Model From Imperfect Training Data for Robust Channel Estimation

Abstract

In this letter, we propose a 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 bs can be utilized for training. This is in sharp contrast to state-of-the-art ml techniques where a training dataset consisting of perfect 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 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.