318 citations · 1k across the 84 of their papers we have counts for
14 papers · 1 filter
On the Mean Square Error Optimal Estimator in One-Bit Quantized Systems
Benedikt Fesl, Michael Koller, Wolfgang Utschick
This paper investigates the mean square error (MSE)-optimal conditional mean estimator (CME) in one-bit quantized systems in the context of channel estimation with jointly Gaussian…
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…
High SNR Analysis of RIS-Aided MIMO Broadcast Channels
Dominik Semmler, Michael Joham, Wolfgang Utschick
We analyze the influence of a reconfigurable intelligent surface (RIS) on the Gram channel eigenvalues in a high signal-to-noise ratio (SNR) scenario. This allows to connect specif…
Evaluation of a Gaussian Mixture Model-based Channel Estimator using Measurement Data
Nurettin Turan, Benedikt Fesl, Moritz Grundei +2
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…
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1
Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity meas…
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios
Lakshman Balasubramanian, Jonas Wurst, Robin Egolf +3
Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the rep…
GMM-based Codebook Construction and Feedback Encoding in FDD Systems
Nurettin Turan, Michael Koller, Benedikt Fesl +3
We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a G…
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…
Channel Estimation based on Gaussian Mixture Models with Structured Covariances
Benedikt Fesl, Michael Joham, Sha Hu +3
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 error…
Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO
Matteo Nerini, Valentina Rizzello, Michael Joham +2
To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division…
Rate Splitting in FDD Massive MIMO Systems Based on the Second Order Statistics of Transmission Channels
Donia Ben Amor, Michael Joham, Wolfgang Utschick
In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output (MaMIMO) system o…