46 citations · 183 across the 29 of their papers we have counts for
17 papers · 1 filter
Learning Successive Interference Cancellation for Low-Complexity Soft-Output MIMO Detection
Benedikt Fesl, Fatih Capar
Low-complexity multiple-input multiple-output (MIMO) detection remains a key challenge in modern wireless systems, particularly for 5G reduced capability (RedCap) and internet-of-t…
Addressing Pilot Contamination in Channel Estimation with Variational Autoencoders
Amar Kasibovic, Benedikt Fesl, Michael Baur +1
Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, P…
Linear and Nonlinear MMSE Estimation in One-Bit Quantized Systems under a Gaussian Mixture Prior
Benedikt Fesl, Wolfgang Utschick
We present new fundamental results for the mean square error (MSE)-optimal conditional mean estimator (CME) in one-bit quantized systems for a Gaussian mixture model (GMM) distribu…
High-SNR Comparison of Linear Precoding and DPC in RIS-Aided MIMO Broadcast Channels
Dominik Semmler, Benedikt Fesl, Michael Joham +1
We compare dirty paper coding (DPC) and linear precoding methods in a reconfigurable intelligent surface (RIS)- aided high-signal-to-noise ratio (SNR) scenario, where the channel b…
Enhancing Channel Estimation in Quantized Systems with a Generative Prior
Benedikt Fesl, Aziz Banna, Wolfgang Utschick
Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative pr…
Channel-Adaptive Pilot Design for FDD-MIMO Systems Utilizing Gaussian Mixture Models
Nurettin Turan, Benedikt Fesl, Benedikt Böck +2
In this work, we propose to utilize Gaussian mixture models (GMMs) to design pilots for downlink (DL) channel estimation in frequency division duplex (FDD) systems. The GMM capture…
Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation
Benedikt Fesl, Michael Baur, Florian Strasser +2
This work proposes a novel channel estimator based on diffusion models (DMs), one of the currently top-rated generative models. Contrary to related works utilizing generative prior…
Wireless Channel Prediction via Gaussian Mixture Models
Nurettin Turan, Benedikt Böck, Kai Jie Chan +5
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) wi…
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
Benedikt Böck, Dominik Semmler, Benedikt Fesl +2
When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing…
Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
Michael Baur, Nurettin Turan, Benedikt Fesl +1
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed c…
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
Benedikt Fesl, Nurettin Turan, Benedikt Böck +1
This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative m…
Leveraging Variational Autoencoders for Parameterized MMSE Estimation
Michael Baur, Benedikt Fesl, Wolfgang Utschick
In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoen…
Low-Rank Structured MMSE Channel Estimation with Mixtures of Factor Analyzers
Benedikt Fesl, Nurettin Turan, Wolfgang Utschick
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 inferre…
Learning a Gaussian Mixture Model from Imperfect Training Data for Robust Channel Estimation
Benedikt Fesl, Nurettin Turan, Michael Joham +1
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…
Channel Estimation with Reduced Phase Allocations in RIS-Aided Systems
Benedikt Fesl, Andreas Faika, Nurettin Turan +2
We consider channel estimation in systems equipped with a reconfigurable intelligent surface (RIS). In order to illuminate the additional cascaded channel as compared to systems wi…
Variational Autoencoder Leveraged MMSE Channel Estimation
Michael Baur, Benedikt Fesl, Michael Koller +1
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 conditiona…
An Asymptotically MSE-Optimal Estimator based on Gaussian Mixture Models
Michael Koller, Benedikt Fesl, Nurettin Turan +1
This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given ch…