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Spectral-Convolutional Factorized Transformer for Joint Multipath Parameter Estimation: Applications to mmWave Positioning and Radio SLAM — arXiv:2611.01491

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01491
License (as archived): CC0-1.0
Archived: 2026-11-03T10:47:38.954Z
SHA-256 of the archived text: c25f57542c224122659a93223b110d3adfebf9a4a70ee46f523f6c578e30eb1d
Stable URL of this page: https://metraipolis.com/documento/dc_cc2ea267aff2

How to cite

Spectral-Convolutional Factorized Transformer for Joint Multipath Parameter Estimation: Applications to mmWave Positioning and Radio SLAM — arXiv:2611.01491 — arXiv (en), https://arxiv.org/abs/2611.01491 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cc2ea267aff2 (Metraipolis archive dc_cc2ea267aff2, sha256 c25f57542c224122659a93223b110d3adfebf9a4a70ee46f523f6c578e30eb1d)

BibTeX

@misc{dccc2ea267aff2,
  title = {Spectral-Convolutional Factorized Transformer for Joint Multipath Parameter Estimation: Applications to mmWave Positioning and Radio SLAM — arXiv:2611.01491},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01491},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cc2ea267aff2; sha256 c25f57542c224122659a93223b110d3adfebf9a4a70ee46f523f6c578e30eb1d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01491. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including positioning and mapping. Current research has already integrated AI into several aspects of communication, particularly wireless channel estimation. However, most learning-based channel estimators focus on communication-oriented objectives rather than explicitly estimating multipath parameters such as delay, angle of arrival (AoA), and angle of departure (AoD). These parameters provide essential geometric information for downstream tasks including positioning and radio simultaneous localization and mapping (SLAM). In this work, we propose a Spectral-Convolutional Factorized Transformer (SCFT) for joint multipath component (MPC) estimation from noisy complex channel estimates and physical beam-pair descriptors. SCFT combines local spectral convolution, permutation-equivariant beam-set attention, and a query-based Transformer decoder to detect and classify line-of-sight (LoS) and non-line-of-sight (NLoS) paths and estimate their observed delays, AoAs, and AoDs. Experiments on realistic ray-tracing data show that SCFT reduces the generalized optimal sub-pattern assignment (GOSPA) error by 13.4% compared with ResNet, the strongest competing baseline. We further demonstrate this advantage in independent snapshot positioning and radio SLAM, where SCFT reduces position RMSE by 18.5% and 65.0%, respectively, relative to ResNet. These findings highlight the importance of explicitly recovering multipath geometry, as improvements in MPC estimation can translate into substantial gains in downstream tasks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01491 — 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 2026-11-03T10:39:50.477Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).