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MambaLoc: Lightweight Indoor Localization Using Cross-Modal Knowledge Distillation — arXiv:2611.10360

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.10360
License (as archived): CC0-1.0
Archived: 2026-11-10T18:40:55.548Z
SHA-256 of the archived text: adb7db5ed76f00967b624b96c1cd858840e91693639417c664a3c7a79f9e8d02
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How to cite

MambaLoc: Lightweight Indoor Localization Using Cross-Modal Knowledge Distillation — arXiv:2611.10360 — arXiv (en), https://arxiv.org/abs/2611.10360 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_49ebee36b747 (Metraipolis archive dc_49ebee36b747, sha256 adb7db5ed76f00967b624b96c1cd858840e91693639417c664a3c7a79f9e8d02)

BibTeX

@misc{dc49ebee36b747,
  title = {MambaLoc: Lightweight Indoor Localization Using Cross-Modal Knowledge Distillation — arXiv:2611.10360},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.10360},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_49ebee36b747; sha256 adb7db5ed76f00967b624b96c1cd858840e91693639417c664a3c7a79f9e8d02},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.10360. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate indoor localization on resource-constrained edge devices remains challenging due to the tension between modeling fidelity and computational efficiency. Wi-Fi Channel State Information (CSI) enables low-cost deployment using existing Wi-Fi infrastructure but is heavily distorted by multipath propagation. Transformer-based solutions mitigate these effects but their quadratic complexity limits real-time edge deployment. Ultra-Wideband (UWB) provides geometrically precise ranging yet requires dedicated infrastructure. We present MambaLoc, a cross-modal distillation framework that improves the accuracy-efficiency balance in CSI-based localization. MambaLoc uses a linear-time Selective State Space Model (Mamba) as a lightweight CSI student, guided during training by a UWB-Transformer teacher. A Feature Alignment Module bridges the representational gap between heterogeneous RF modalities, allowing the CSI model to inherit UWB-derived geometric structure without UWB hardware at inference. A probabilistic Gaussian Mixture regression head models multi-modal spatial ambiguity from multipath effects, avoiding the physically implausible averaging of deterministic regression. Evaluations on a real-world dataset show MambaLoc achieves a median localization error of 0.230 m, reducing error by 22.2% over a standalone Mamba baseline, while requiring 65.1% fewer FLOPs and 58% lower inference latency than Transformer-based approaches.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2611.10360 — 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 2026-11-10T18:37:37.488Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).