318 citations · 1k across the 84 of their papers we have counts for
22 papers · 1 filter
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…
A Zero-Forcing Approach for the RIS-Aided MIMO Broadcast Channel
Dominik Semmler, Michael Joham, Wolfgang Utschick
We present efficient algorithms for the sum-spectral efficiency (SE) maximization of the multi-user reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (M…
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…
Imperfect photon detection in quantum illumination
F. Kronowetter, M. Würth, W. Utschick +2
In quantum illumination, various detection schemes have been proposed for harnessing remaining quantum correlations of the entanglement-based resource state. To this date, the only…
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…
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.…
A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
Marion Neumeier, Andreas Tollkühn, Michael Botsch +1
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GN…
On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows
Jia Yu Tee, Oliver De Candido, Wolfgang Utschick +1
Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distr…
Enhanced Low-Complexity FDD System Feedback with Variable Bit Lengths via Generative Modeling
Nurettin Turan, Benedikt Fesl, Wolfgang Utschick
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 flexibilit…
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…
Gradient Derivation for Learnable Parameters in Graph Attention Networks
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powe…
A Versatile Low-Complexity Feedback Scheme for FDD Systems via Generative Modeling
Nurettin Turan, Benedikt Fesl, Michael Koller +2
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 utili…
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…
Reverse Ordering Techniques for Attention-Based Channel Prediction
Valentina Rizzello, Benedikt Böck, Michael Joham +1
This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transforme…
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…