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MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation — arXiv:2608.05076

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05076
License (as archived): CC0-1.0
Archived: 2026-10-31T11:15:21.103Z
SHA-256 of the archived text: e6c5c6d12426b060f1a2569620a98ff41caba8ccf0fafcc46bc64fafb09f3eec
Stable URL of this page: https://metraipolis.com/documento/dc_2dcf52bf161b

How to cite

MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation — arXiv:2608.05076 — arXiv (en), https://arxiv.org/abs/2608.05076 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2dcf52bf161b (Metraipolis archive dc_2dcf52bf161b, sha256 e6c5c6d12426b060f1a2569620a98ff41caba8ccf0fafcc46bc64fafb09f3eec)

BibTeX

@misc{dc2dcf52bf161b,
  title = {MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation — arXiv:2608.05076},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05076},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2dcf52bf161b; sha256 e6c5c6d12426b060f1a2569620a98ff41caba8ccf0fafcc46bc64fafb09f3eec},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05076. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals. Existing wireless foundation models typically pretrain on channel tensors using masked reconstruction over subcarriers, antennas, or time but ignore the physical characteristics of wireless propagation. In this work, we propose to instead use multipath propagation as the fundamental pretraining object. We present MultiPathFormer, an autoregressive foundation model that represents each transmitter-receiver link as an ordered sequence of continuous-valued path tokens and pretrains with next-path prediction. We introduce an Environmental RAG (retrieval-augmented generation) mechanism and a first-path codebook on top of the transformer backbone, leveraging environment knowledge to improve path statistics estimation like delay and power by up to 59%. MultiPathFormer pretrained on 27 environments transfers to unseen users and, after scenario-specific fine-tuning, outperforms training the corresponding models from scratch in new environments. Across downstream tasks, it outperforms SOTA channel-based foundation models, achieving 5.57 m mean localization error, 0.914 top-3 beam accuracy, 0.994 line-of-sight classification accuracy, and 0.561 channel estimation NMSE. These results show that path-level pretraining can learn reusable representations of wireless propagation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-06 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.05076 — 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 2026-10-31T11:02:13.574Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).