# Augmenting Channel Charting with Classical Wireless Source Localization Techniques

arXiv: https://arxiv.org/abs/2312.01968 · HTML: https://mlsanity.com/paper/2312.01968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [CSI Compression using Channel Charting](https://mlsanity.com/paper/2501.01431/) (2024)
- [Global Scale Self-Supervised Channel Charting with Sensor Fusion](https://mlsanity.com/paper/2405.04357/) (2024)
- [CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems](https://mlsanity.com/paper/2508.02553/) (2025)

## Abstract (arXiv)

Channel Charting aims to construct a map of the radio environment by leveraging similarity relationships found in high-dimensional channel state information. Although resulting channel charts usually accurately represent local neighborhood relationships, even under conditions with strong multipath propagation, they often fall short in capturing global geometric features. On the other hand, classical model-based localization methods, such as triangulation and multilateration, can easily localize signal sources in the global coordinate frame. However, these methods rely heavily on the assumption of line-of-sight channels and distributed antenna deployments. Based on measured data, we compare classical source localization techniques to channel charts with respect to localization performance. We suggest and evaluate methods to enhance Channel Charting with model-based localization approaches: One approach involves using information derived from classical localization methods to map channel chart locations to physical positions after conventional training of the forward charting function. Foremost, though, we suggest to incorporate information from model-based approaches during the training of the forward charting function in what we call "augmented Channel Charting". We demonstrate that Channel Charting can outperform classical localization methods on the considered dataset.