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Wireless Foundation Models: State-of-the-Art and Open Challenges — arXiv:2609.04707

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04707
License (as archived): CC0-1.0
Archived: 2026-10-16T22:26:33.599Z
SHA-256 of the archived text: f89ce0952ef2422e2ca92eaa5817dd48ccf2fec99125ed52fe638ec9e51b3d32
Stable URL of this page: https://metraipolis.com/documento/dc_94a80da4183b

How to cite

Wireless Foundation Models: State-of-the-Art and Open Challenges — arXiv:2609.04707 — arXiv (en), https://arxiv.org/abs/2609.04707 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_94a80da4183b (Metraipolis archive dc_94a80da4183b, sha256 f89ce0952ef2422e2ca92eaa5817dd48ccf2fec99125ed52fe638ec9e51b3d32)

BibTeX

@misc{dc94a80da4183b,
  title = {Wireless Foundation Models: State-of-the-Art and Open Challenges — arXiv:2609.04707},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04707},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_94a80da4183b; sha256 f89ce0952ef2422e2ca92eaa5817dd48ccf2fec99125ed52fe638ec9e51b3d32},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04707. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. However, the rapidly growing literature remains fragmented across modalities, pretraining objectives, architectures, adaptation strategies, and evaluation protocols, making it difficult to assess progress toward broadly transferable models. This survey provides a systematic analysis of WFMs for physical-layer applications. We first introduce the main WFM design components, including pretraining, backbone architectures, and downstream adaptation. We then organize the literature into five physical-layer task families: signal recognition and demodulation, channel representation learning, RF sensing and localization, beam management, and spectrum sensing and monitoring, while separately examining multi-task PHY models. Across these categories, we analyze how existing models are pretrained, adapted, and evaluated, with particular attention to downstream task diversity and the distinction between in-distribution, partial-shift, and out-of-distribution transfer. Our analysis shows that current WFMs provide increasing evidence of reusable wireless representations, but this evidence varies considerably across task families and evaluation settings. Differences in datasets, modalities, architectures, pretraining objectives, adaptation protocols, and distribution shifts make it difficult to determine which design choices drive transfer and generalization. We conclude by identifying open directions for improving data availability, evaluation rigor, generalization, efficient adaptation, and real-world deployment, providing a unified framework for understanding the current WFM landscape and the requirements for developing more reusable foundation models for future physical-layer wireless systems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-07 in cs.AI).
Categorie arXiv: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.04707 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:18:11.518Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).