Dynamic User Grouping based on Location and Heading in 5G NR Systems
This work addresses network optimization for 5G users, but it appears incremental as it applies existing machine learning techniques to a specific domain.
The paper tackled the problem of dynamic user grouping in 5G NR systems by using Sounding Reference Signals channel fingerprints with machine learning methods like neural networks and clustering, resulting in improved network performance, user experience, and service delivery as demonstrated in a commercial deployment.
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.