8 papers
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…
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…
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)…
Sparse Bayesian Generative Modeling for Joint Parameter and Channel Estimation
Benedikt Böck, Franz WeiÃer, 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…
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,…