Physics-Informed Generative Modeling of Wireless Channels

Benedikt Böck, Andreas Oeldemann, Timo Mayer, Francesco Rossetto, Wolfgang Utschick
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:4602-4626, 2025.

Abstract

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 wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved 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 compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative 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) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-bock25a, title = {Physics-Informed Generative Modeling of Wireless Channels}, author = {B\"{o}ck, Benedikt and Oeldemann, Andreas and Mayer, Timo and Rossetto, Francesco and Utschick, Wolfgang}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {4602--4626}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/bock25a/bock25a.pdf}, url = {https://proceedings.mlr.press/v267/bock25a.html}, abstract = {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 wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved 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 compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative 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) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.} }
Endnote
%0 Conference Paper %T Physics-Informed Generative Modeling of Wireless Channels %A Benedikt Böck %A Andreas Oeldemann %A Timo Mayer %A Francesco Rossetto %A Wolfgang Utschick %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-bock25a %I PMLR %P 4602--4626 %U https://proceedings.mlr.press/v267/bock25a.html %V 267 %X 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 wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved 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 compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative 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) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.
APA
Böck, B., Oeldemann, A., Mayer, T., Rossetto, F. & Utschick, W.. (2025). Physics-Informed Generative Modeling of Wireless Channels. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:4602-4626 Available from https://proceedings.mlr.press/v267/bock25a.html.

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