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eess.SP2025

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…

eess.SP2025

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…

eess.SP2025

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…

eess.SP2024

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…

eess.SP2024

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…