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

TL;DR

This paper presents a method for dynamic user grouping in 5G NR systems using location and heading data, leveraging machine learning to enhance network performance and user experience.

Contribution

It introduces a novel approach combining sounding reference signals, machine learning, and clustering for real-time user grouping in 5G networks.

Findings

Effective user grouping based on location and heading

Improved network performance through dynamic grouping

Feasibility demonstrated in commercial 5G deployment

Abstract

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.