# Improving Channel Charting with Representation-Constrained Autoencoders

arXiv: https://arxiv.org/abs/1908.02878 · HTML: https://mlsanity.com/paper/1908.02878/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting](https://mlsanity.com/paper/2204.13996/) (2022)
- [Efficient channel charting via phase-insensitive distance computation](https://mlsanity.com/paper/2104.13184/) (2021)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting](https://mlsanity.com/paper/2310.03762/) (2023)
- [Velocity-Based Channel Charting with Spatial Distribution Map Matching](https://mlsanity.com/paper/2311.08016/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Semi-supervised t-SNE for Millimeter-wave Wireless Localization](https://mlsanity.com/paper/2111.13573/) (2021)
- [On the Use of CSI for the Generation of RF Fingerprints and Secret Keys](https://mlsanity.com/paper/2110.15415/) (2021)
- [Global Scale Self-Supervised Channel Charting with Sensor Fusion](https://mlsanity.com/paper/2405.04357/) (2024)
- [Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2511.00919/) (2025)

## Abstract (arXiv)

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.