Channel Charting in Real-World Coordinates
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Abstract (arXiv)
Channel charting is an emerging self-supervised method that maps channel state information (CSI) to a low-dimensional latent space, which represents pseudo-positions of user equipments (UEs). While this latent space preserves local geometry, i.e., nearby UEs are nearby in latent space, the pseudo-positions are in arbitrary coordinates and global geometry is not preserved. In order to enable channel charting in real-world coordinates, we propose a novel bilateration loss for multipoint wireless systems in which only the access point (AP) locations are known--no geometrical models or ground-truth UE position information is required. The idea behind this bilateration loss is to compare the received power at pairs of APs in order to determine whether a UE should be placed closer to one AP or the other in latent space. We demonstrate the efficacy of our method using channel vectors from a commercial ray-tracer.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Aug 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2.
5 papers.
- 2023Velocity-Based Channel Charting with Spatial Distribution Map Matchingcited by 5
- 2024CSI Compression using Channel Chartingcited by 1
- 2025Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platformcited by 1
- 2025Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inferencecited by 1
- 2024Global Scale Self-Supervised Channel Charting with Sensor Fusioncited by 0
- 2025Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference4 Dec 2025
- 2025Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform24 Apr 2025
- 2024CSI Compression using Channel Charting16 Dec 2024
- 2024Global Scale Self-Supervised Channel Charting with Sensor Fusion7 May 2024
- 2023Velocity-Based Channel Charting with Spatial Distribution Map Matching14 Nov 2023