# Siamese Neural Networks for Wireless Positioning and Channel Charting

arXiv: https://arxiv.org/abs/1909.13355 · HTML: https://mlsanity.com/paper/1909.13355/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 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)
- [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)
- [Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning](https://mlsanity.com/paper/2204.12796/) (2022)
- [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)
- [NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks](https://mlsanity.com/paper/2606.11914/) (2026)

## Abstract (arXiv)

Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna base-station in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks requires a dataset that contains both CSI and associated location information. Channel charting (CC) only requires CSI information to extract relative position information. Since CC builds on dimensionality reduction, it can be implemented using autoencoders. In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting. In addition, our framework enables semisupervised positioning, where only a small set of location information is available during training. We use simulations to demonstrate that Siamese networks achieve similar or better performance than existing positioning and CC approaches with a single, unified neural network architecture.