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 …

In this work, we use real-world data in order to evaluate and validate a machine learning
(ML)-based algorithm for physical layer functionalities. Specifically, we apply a recently …

In this work, we propose variations of a Gaussian mixture model (GMM) based channel
estimator that was recently proven to be asymptotically optimal in the minimum mean square …

This paper investigates a channel estimator based on Gaussian mixture models (GMMs).
We fit a GMM to given channel samples to obtain an analytic probability density function …

This work proposes a generative modeling-aided channel estimator based on mixtures of
factor analyzers (MFA). In an offline step, the parameters of the generative model are …

In this paper, we address the problem of identifying convolutive channels using a Gaussian
maximum-likelihood (ML) approach when short training sequences (possibly shorter than …

This paper investigates the combination of parametric channel estimation with minimum
mean square error (MMSE) estimation. We propose a two-stage channel estimation …

New communication standards need to deal with machine-to-machine communications, in
which users may start or stop transmitting at any time in an asynchronous manner. Thus, the …

In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which
is learned on imperfect training data, ie, the training data are solely comprised of noisy and …

In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers
based on pilot symbol observations. The inherent sparse nature of wireless multipath …