# Semi-supervised t-SNE for Millimeter-wave Wireless Localization

arXiv: https://arxiv.org/abs/2111.13573 · HTML: https://mlsanity.com/paper/2111.13573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [Improving Channel Charting with Representation-Constrained Autoencoders](https://mlsanity.com/paper/1908.02878/) (2019)
- [Siamese Neural Networks for Wireless Positioning and Channel Charting](https://mlsanity.com/paper/1909.13355/) (2019)
- [Massive MIMO Channel Measurements and Achievable Rates in a Residential Area](https://mlsanity.com/paper/2002.09452/) (2020)

## Built on by (most cited first, 3 of 3)

- [Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning](https://mlsanity.com/paper/2204.12796/) (2022)
- [Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins](https://mlsanity.com/paper/2508.09055/) (2025)
- [Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2511.00919/) (2025)

## Abstract (arXiv)

We consider the mobile localization problem in future millimeter-wave wireless networks with distributed Base Stations (BSs) based on multi-antenna channel state information (CSI). For this problem, we propose a Semi-supervised tdistributed Stochastic Neighbor Embedding (St-SNE) algorithm to directly embed the high-dimensional CSI samples into the 2D geographical map. We evaluate the performance of St-SNE in a simulated urban outdoor millimeter-wave radio access network. Our results show that St-SNE achieves a mean localization error of 6.8 m with only 5% of labeled CSI samples in a 200*200 m^2 area with a ray-tracing channel model. St-SNE does not require accurate synchronization among multiple BSs, and is promising for future large-scale millimeter-wave localization.