用5G信号指纹动态分组用户，提升网络性能。
Dynamic User Grouping based on Location and Heading in 5G NR Systems
- 通过5G信号指纹结合机器学习实现用户动态分组
- 基于用户位置与朝向方向精准分组，提升资源调度效率
- 适合5G网络优化与智能调度场景应用
在第五代移动通信（5G）新无线（NR）系统中，基于地理定位的用户分组具有显著提升网络性能、用户体验和服务交付能力的应用潜力。本文展示了如何利用导频参考信号（Sounding Reference Signals）的信道指纹，在真实的5G NR商用部署中，结合神经网络与聚类算法，根据用户的室外位置和朝向方向实现动态用户分组。该方法可实时捕捉用户空间分布特征，为基站的波束管理、资源分配及干扰协调提供精准支持。
原文摘要 · Abstract (English)
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.
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