ICML 2025

Physics-Informed Generative Modeling of Wireless Channels
Benedikt B¨ock 1 Andreas Oeldemann 2 Timo Mayer 2 Francesco Rossetto 2 Wolfgang Utschick 1
Abstract
Learning the site-specific distribution of the wire-
less channel within a particular environment of
interest is essential to exploit the full potential of
machine learning (ML) for wireless communica-
tions and radar applications. Generative model-
ing offers a promising framework to address this
problem. However, existing approaches pose un-
resolved challenges, including the need for high-
quality training data, limited generalizability, and
a lack of physical interpretability. To address
these issues, we combine the physics-related com-
pressibility of wireless channels with generative
modeling, in particular, sparse Bayesian genera-
tive modeling (SBGM), to learn the distribution of
the underlying physical channel parameters. By
leveraging the sparsity-inducing characteristics of
SBGM, our methods can learn from compressed
observations received by an access point (AP) dur-
ing default online operation. Moreover, they are
physically interpretable and generalize over sys-
tem configurations without requiring retraining.
1. Introduction
The accurate modeling of wireless channels is critical for
system design, network optimization, and performance eval-
uation in wireless communications and radar (Wang et al.,
2018; 2020; Yin & Cheng, 2016). There are two approaches
to wireless channel modeling: physically and analytically
oriented (Almers et al., 2007). The former integrates the
underlying physics in the description of wireless channels
and focuses on their accuracy and realism. In contrast, the
latter captures channel statistics, ignoring the underlying
physics, and provides an easy-to-use framework for evalua-
tion and system design schemes (Almers et al., 2007; Imoize
et al., 2021). Standardized channel models such as the
3GPP (3GPP, 2024a), COST (Liu et al., 2012) or WINNER
1Technical University of Munich...

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