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Rethinking Channel Charting: A Graph Perspective — arXiv:2609.06204

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.06204
License (as archived): CC0-1.0
Archived: 2026-10-16T14:09:21.268Z
SHA-256 of the archived text: 8da4e0ac9e88f0dc43189691a00d302aea7143b66a000ddc7ee610b361636258
Stable URL of this page: https://metraipolis.com/documento/dc_61682d6c1936

How to cite

Rethinking Channel Charting: A Graph Perspective — arXiv:2609.06204 — arXiv (en), https://arxiv.org/abs/2609.06204 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_61682d6c1936 (Metraipolis archive dc_61682d6c1936, sha256 8da4e0ac9e88f0dc43189691a00d302aea7143b66a000ddc7ee610b361636258)

BibTeX

@misc{dc61682d6c1936,
  title = {Rethinking Channel Charting: A Graph Perspective — arXiv:2609.06204},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.06204},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_61682d6c1936; sha256 8da4e0ac9e88f0dc43189691a00d302aea7143b66a000ddc7ee610b361636258},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.06204. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Channel charting is a self-supervised framework that learns low-dimensional spatial representations from high-dimensional channel state information. We revisit channel charting from a graph-theoretic perspective, and show that the position-diffusion objective is equivalent to a graph Laplacian smoothness functional. We propose a Graph Neural Network (GNN) formulation that replaces the Siamese network's global geodesic dissimilarity objective with a graph smoothness objective. We consider the reformulated objective to be the position diffusion objective. Without diverging from the original optimization objective, the GNN replaces the quadratic-cost self-correlation encoding with linear-cost message passing over an Angle-Delay Profile (ADP)-similarity graph, achieving comparable positioning accuracy with 512 times fewer parameters. Using Laplacian spectral analysis, we demonstrate that obstacles compress the ADP graph spectrum, while the GNN acts as a spectral decompressor against obstacles and other environmental semantics, but as a compressor against excessive ADP embedding space. Beyond this, the second eigenvector of the learned embedding encodes the line-of-sight/non-line-of-sight boundary rather than spatial coordinates.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-09 in cs.NI).
Categorie arXiv: Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2609.06204 — 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 2026-10-16T13:58:54.225Z dalla pagina https://arxiv.org/catchup/cs.NI/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).