AI-Powered Dynamic Flow Steering in 5G Networks: RAN Analytics Integration with UPF for Enhanced QoS Management

Abstract

This article presents an integrated framework for AI-powered dynamic flow steering in 5G networks that leverages real-time Radio Access Network (RAN) analytics to optimize traffic management through the User Plane Function (UPF). The technical architecture establishes seamless integration between Network Data Analytics Function (NWDAF) components and distributed UPF instances, enabling intelligent routing decisions based on current radio conditions. Machine learning algorithms, particularly reinforcement learning models, provide adaptive optimization capabilities that continuously refine steering policies through closed-loop feedback mechanisms. Performance validation across diverse deployment scenarios demonstrates that this approach maintains superior throughput and latency metrics compared to traditional management techniques, especially in challenging high-density environments and industrial IoT applications. The integration of predictive congestion management with computational offloading strategies creates a comprehensive optimization framework that addresses both network efficiency and application performance, establishing a foundation for future-ready, user-centric network experiences in evolving 5G architectures.