We propose a versatile feedback scheme for both single-and multi-user multiple-input
multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose …

This work introduces a novel class of channel estimators tailored for coarse quantization
systems. The proposed estimators are founded on conditionally Gaussian latent generative …

We propose to utilize a variational autoencoder (VAE) for data-driven channel estimation.
The underlying true and unknown channel distribution is modeled by the VAE as a …

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 …

In this manuscript, we propose to utilize the generative neural network-based variational
autoencoder for channel estimation. The variational autoencoder models the underlying true …

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 …

Recently, a versatile limited feedback scheme based on a Gaussian mixture model (GMM)
was proposed for frequency division duplex (FDD) systems. This scheme provides high …

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 …

Diffusion probabilistic models (DPMs) have recently shown great potential for denoising
tasks. Despite their practical utility, there is a notable gap in their theoretical understanding …

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 …