Metraipolis › archive › dc_7fa700339727

DyRadio: Spatiotemporal Diffusion for Dynamic Radio Map Reconstruction — arXiv:2611.01603

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01603
License (as archived): CC0-1.0
Archived: 2026-11-03T11:06:35.812Z
SHA-256 of the archived text: 6aef753173e46901c38155a6074a200d716023f6c8ce0c550c585dbc0f2775dc
Stable URL of this page: https://metraipolis.com/documento/dc_7fa700339727

How to cite

DyRadio: Spatiotemporal Diffusion for Dynamic Radio Map Reconstruction — arXiv:2611.01603 — arXiv (en), https://arxiv.org/abs/2611.01603 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7fa700339727 (Metraipolis archive dc_7fa700339727, sha256 6aef753173e46901c38155a6074a200d716023f6c8ce0c550c585dbc0f2775dc)

BibTeX

@misc{dc7fa700339727,
  title = {DyRadio: Spatiotemporal Diffusion for Dynamic Radio Map Reconstruction — arXiv:2611.01603},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01603},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7fa700339727; sha256 6aef753173e46901c38155a6074a200d716023f6c8ce0c550c585dbc0f2775dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01603. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate radio map (RM) reconstruction is essential for environment-aware wireless networks. Sampling-based methods, which infer radio conditions from sparse measurements, are widely adopted because they do not require explicit knowledge of transmitter configurations. However, in dynamic environments, time-varying propagation conditions cause RMs to evolve, complicating reconstruction from temporally distributed sparse observations. Although cross-frame observations provide complementary spatial information, their direct aggregation can introduce temporal state mismatch. Furthermore, unknown measurement noise adds to the reconstruction difficulties. To address these challenges, we propose DyRadio, a framework that integrates spatiotemporal RM reconstruction with unknown noise estimation. Specifically, a propagation-prior-guided, noise-conditioned spatiotemporal diffusion model (DM) jointly reconstructs consecutive RMs by capturing cross-frame dependencies, while adapting to measurement noise levels. To estimate the noise level, an iterative predictive likelihood method alternates between DM-based blind-area prediction and noise-level updates. The estimated noise level then conditions reconstruction of the complete RM sequence. Experimental results demonstrate superior reconstruction accuracy and robustness against measurement noise, with peak signal-to-noise ratio (PSNR) improvements of 1.18-5.75 dB over the best-performing baseline across all evaluated settings.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01603 — 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 2026-11-03T10:59:46.030Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).