11 citations · 22 across the 17 of their papers we have counts for
19 papers
OTFS Channel Estimation Utilizing Sparse Bayesian Generative Modelling
Louis Anseaume, Benedikt Böck, Franz Weißer +1
One of the key challenges of future wireless communication systems is ensuring reliability in high-speed mobile scenarios, where accurate recovery of channel state information (CSI…
Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
Benedikt Böck, Amar Kasibovic, Wolfgang Utschick
Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this wo…
Precoder Design in Multi-User FDD Systems with VQ-VAE and GNN
Srikar Allaparapu, Michael Baur, Benedikt Böck +2
Robust precoding is efficiently feasible in frequency division duplex (FDD) systems by incorporating the learnt statistics of the propagation environment through a generative model…
Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
Kathrin Klein, Benedikt Böck, Nurettin Turan +1
This work addresses the challenge of making generative models suitable for resource-constrained environments like mobile wireless communication systems. We propose a generative mod…
Sparse Bayesian Generative Modeling for Joint Parameter and Channel Estimation
Benedikt Böck, Franz Weißer, Michael Baur +1
Leveraging the inherent connection between sensing systems and wireless communications can improve their overall performance and is the core objective of joint communications and s…
Physics-Informed Generative Modeling of Wireless Channels
Benedikt Böck, Andreas Oeldemann, Timo Mayer +2
Learning the site-specific distribution of the wireless channel within a particular environment of interest is essential to exploit the full potential of machine learning (ML) for…
Statistical Precoder Design in Multi-User Systems via Graph Neural Networks and Generative Modeling
Nurettin Turan, Srikar Allaparapu, Donia Ben Amor +3
This letter proposes a graph neural network (GNN)-based framework for statistical precoder design that leverages model-based insights to compactly represent statistical knowledge,…
Decoupling Networks and Super-Quadratic Gains for RIS Systems with Mutual Coupling
Dominik Semmler, Josef A. Nossek, Michael Joham +2
We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particul…
Sparse Bayesian Generative Modeling for Compressive Sensing
Benedikt Böck, Sadaf Syed, Wolfgang Utschick
This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes idea…
A Versatile Pilot Design Scheme for FDD Systems Utilizing Gaussian Mixture Models
Nurettin Turan, Benedikt Böck, Benedikt Fesl +3
In this work, we propose a Gaussian mixture model (GMM)-based pilot design scheme for downlink (DL) channel estimation in single- and multi-user multiple-input multiple-output (MIM…
A Statistical Characterization of Wireless Channels Conditioned on Side Information
Benedikt Böck, Michael Baur, Nurettin Turan +2
Statistical prior channel knowledge, such as the wide-sense-stationary-uncorrelated-scattering (WSSUS) property, and additional side information both can be used to enhance physica…
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…
On the Asymptotic Mean Square Error Optimality of Diffusion Models
Benedikt Fesl, Benedikt Böck, Florian Strasser +3
Diffusion models (DMs) as generative priors have recently shown great potential for denoising tasks but lack theoretical understanding with respect to their mean square error (MSE)…
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…
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…
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…
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…
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…
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…