# Triplet-Based Wireless Channel Charting: Architecture and Experiments

arXiv: https://arxiv.org/abs/2005.12242 · HTML: https://mlsanity.com/paper/2005.12242/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 17 of 17)

- [Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting](https://mlsanity.com/paper/2204.13996/) (2022)
- [Wireless Channel Charting: Theory, Practice, and Applications](https://mlsanity.com/paper/2304.08095/) (2023)
- [Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach](https://mlsanity.com/paper/2210.06294/) (2022)
- [Efficient Deep Unfolding for SISO-OFDM Channel Estimation](https://mlsanity.com/paper/2210.06588/) (2022)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [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)
- [Blind Construction of Angular Power Maps in Massive MIMO Networks](https://mlsanity.com/paper/2510.07071/) (2025)
- [Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning](https://mlsanity.com/paper/2204.12796/) (2022)
- [Distributed Coordination for Heterogeneous Non-Terrestrial Networks](https://mlsanity.com/paper/2502.17366/) (2025)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments](https://mlsanity.com/paper/2510.08015/) (2025)
- [Global Scale Self-Supervised Channel Charting with Sensor Fusion](https://mlsanity.com/paper/2405.04357/) (2024)
- [LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability](https://mlsanity.com/paper/2605.24077/) (2026)
- [RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models](https://mlsanity.com/paper/2606.01899/) (2026)

## Abstract (arXiv)

Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting the fundamental parameters governing its distribution. We introduce a novel channel charting approach based on triplets of samples. The proposed algorithm learns a meaningful similarity metric between CSI samples on the basis of proximity in their respective acquisition times, and simultaneously performs dimensionality reduction. We present an extensive experimental validation of the proposed approach on data obtained from a commercial Massive MIMO system; in particular, we evaluate to which extent the obtained channel chart is similar to the user location information, although it is not supervised by any geographical data. Finally, we propose and evaluate variations in the channel charting process, including the partially supervised case where some labels are available for part of the dataset.