# Channel Charting in Real-World Coordinates

arXiv: https://arxiv.org/abs/2308.14498 · HTML: https://mlsanity.com/paper/2308.14498/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2023.

## Built on by (most cited first, 5 of 5)

- [Velocity-Based Channel Charting with Spatial Distribution Map Matching](https://mlsanity.com/paper/2311.08016/) (2023)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference](https://mlsanity.com/paper/2512.13701/) (2025)
- [Global Scale Self-Supervised Channel Charting with Sensor Fusion](https://mlsanity.com/paper/2405.04357/) (2024)

## 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.