# ML-Enabled Outdoor User Positioning in 5G NR Systems via Uplink SRS Channel Estimates

arXiv: https://arxiv.org/abs/2304.06514 · HTML: https://mlsanity.com/paper/2304.06514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G](https://mlsanity.com/paper/2001.00977/) (2020)
- [Positioning in 5G networks](https://mlsanity.com/paper/2102.03361/) (2021)
- [ML-Assisted UE Positioning: Performance Analysis and 5G Architecture Enhancements](https://mlsanity.com/paper/2108.11365/) (2021)

## Abstract (arXiv)

Cellular user positioning is a promising service provided by Fifth Generation New Radio (5G NR) networks. Besides, Machine Learning (ML) techniques are foreseen to become an integrated part of 5G NR systems improving radio performance and reducing complexity. In this paper, we investigate ML techniques for positioning using 5G NR fingerprints consisting of uplink channel estimates from the physical layer channel. We show that it is possible to use Sounding Reference Signals (SRS) channel fingerprints to provide sufficient data to infer user position. Furthermore, we show that small fully-connected moderately Deep Neural Networks, even when applied to very sparse SRS data, can achieve successful outdoor user positioning with meter-level accuracy in a commercial 5G environment.