A Network Architecture for Scalable End-to-End Management of Reusable AI-Based Applications in 6G Networks

Abstract

This article presents a comprehensive network architecture for managing reusable AI-based applications in 6G networks, addressing the critical challenge of AI silos in current implementations. It introduces a unified approach to data collection, feature extraction, model management, and application integration across network domains. By implementing standardized workflows and shared resources, the architecture enables efficient end-to-end management while promoting reusability and scalability. The solution incorporates a unified data collection layer, shared feature repository, model management framework, and application integration layer, all designed to support the demanding requirements of next-generation networks. Through multiple use cases including RAN optimization, network security, and service quality management, the article demonstrate the architecture's effectiveness in real-world scenarios. The results show significant improvements in development efficiency, resource utilization, scalability, and maintenance operations. It contributes to the evolution of 6G networks by providing a structured approach to integrating AI capabilities while preventing the formation of isolated solutions.