318 citations · 1k across the 84 of their papers we have counts for
33 papers · 1 filter
Design of a Multi-User RIS-Aided System with Statistical Channel Knowledge
Sadaf Syed, Dominik Semmler, Donia Ben Amor +2
Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral and energy efficiency in a wireless communication system. The design of the phase shifts…
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…
An Efficient Rate Splitting Precoding Approach in Multi-User MISO FDD Systems
Donia Ben Amor, Michael Joham, Wolfgang Utschick
In this work, we develop an efficient precoding strategy for a multi-user multiple-input-single output (MU MISO) system operating in frequency-division-duplex (FDD) mode, where rat…
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…
Highly Accelerated Weighted MMSE Algorithms for Designing Precoders in FDD Systems with Incomplete CSI
Donia Ben Amor, Michael Joham, Wolfgang Utschick
In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency…
DoA-Aided MMSE Channel Estimation for Wireless Communication Systems
Franz Weißer, Nurettin Turan, Wolfgang Utschick
This paper investigates the combination of parametric channel estimation with minimum mean square error (MMSE) estimation. We propose a direction-of-arrival (DoA)-aided two-stage c…
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
Michael Baur, Benedikt Böck, Nurettin Turan +1
This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and p…
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…
Data-Aided Channel Estimation Utilizing Gaussian Mixture Models
Franz Weißer, Nurettin Turan, Dominik Semmler +1
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 cha…
Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge
Sadaf Syed, Dominik Semmler, Donia Ben Amor +2
Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cos…
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…
Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis
Josef A. Nossek, Dominik Semmler, Michael Joham +1
RISs are an emerging technology for engineering the channels of future wireless communication systems. The vast majority of research publications on RIS are focussing on system-lev…
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…
Asymptotic Behavior of Zero-Forcing Precoding based on Imperfect Channel Knowledge for Massive MISO FDD Systems
Donia Ben Amor, Michael Joham, Wolfgang Utschick
In this work, we study the asymptotic behavior of the zero-forcing precoder based on the least squares (LS) and the linear minimum mean-square error (LMMSE) channel estimates for t…
Variational Inference Aided Estimation of Time Varying Channels
Benedikt Böck, Michael Baur, Valentina Rizzello +1
One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations. In this context, Dynamical VAEs (DVAEs) build a promising deep lear…
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…
Unsupervised Parameter Estimation using Model-based Decoder
Franz Weißer, Michael Baur, Wolfgang Utschick
In this work, we consider the use of a model-based decoder in combination with an unsupervised learning strategy for direction-of-arrival (DoA) estimation. Relying only on unlabele…
Model Order Selection with Variational Autoencoding
Michael Baur, Franz Weißer, Benedikt Böck +1
Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning-based methods are promising alternatives for such challenging situa…