# Dynamic User Grouping based on Location and Heading in 5G NR Systems

arXiv: https://arxiv.org/abs/2410.19854 · HTML: https://mlsanity.com/paper/2410.19854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 22 Oct 2024.

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

- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G](https://mlsanity.com/paper/2001.00977/) (2020)
- [Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation](https://mlsanity.com/paper/2309.02121/) (2023)
- [Attention-aided Outdoor Localization in Commercial 5G NR Systems](https://mlsanity.com/paper/2405.09715/) (2024)
- [Learning-Based UE Classification in Millimeter-Wave Cellular Systems With Mobility](https://mlsanity.com/paper/2109.05893/) (2021)

## Abstract (arXiv)

User grouping based on geographic location in fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods.