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Found 13 papers, 5 papers with code
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Once shared with the mobile terminal (MT), the GMM is utilized to determine a feedback index at the MT in the online phase.
Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation
This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models.
On the Asymptotic Mean Square Error Optimality of Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs) have recently shown great potential for denoising tasks.
Wireless Channel Prediction via Gaussian Mixture Models
In this work, we utilize a Gaussian mixture model (GMM) to capture the underlying probability density function (PDF) of the channel trajectories of moving mobile terminals (MTs) within the coverage area of a base station (BS) in an offline phase.
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses.
Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems.
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
Conditioning on the latent variable of these generative models yields a locally Gaussian channel distribution, thus enabling the application of the well-known Bussgang decomposition.
Leveraging Variational Autoencoders for Parameterized MMSE Estimation
We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases.
Low-Rank Structured MMSE Channel Estimation with Mixtures of Factor Analyzers
This work proposes a generative modeling-aided channel estimator based on mixtures of factor analyzers (MFA).
Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
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.
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS.
Variational Autoencoder Leveraged MMSE Channel Estimation
First, we show that given perfectly known channel state information at the input of the VAE during estimation, which is impractical, we obtain an estimator that can serve as a benchmark result for an estimation scenario.
An Asymptotically MSE-Optimal Estimator based on Gaussian Mixture Models
Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF.
