A Unified Connectivity and AI-Driven Intelligent Framework for Mobile Networks

Abstract

The burgeoning changes involved in 5G networks bring forth unseen opportunities and challenges in the provision and support of bright, scalable and adaptable services across a wide range of applications. The richness of heterogeneity, time-varying service needs, and the demanding one-order latency necessitate new solutions that far exceed traditional network slicing. The proposed unified intelligent framework of intelligent network slicing, i.e., Intelligence Slicing, is dedicated to cross-domain resource orchestration and service optimization. The framework incorporates the use of Artificial Intelligence (AI) at the center of network slicing mechanisms that enable the autonomous management and optimization of slices, in the domains comprising the RAN, the transport network, the core network, and cloud/edge computing. We study methods such as Reinforcement Learning (RL), Federated Learning (FL), and Graph Neural Networks (GNN) for learning decentralized intelligence, and design them so that optimizing them in real time to traffic requirements, user mobility and Service Level Agreement (SLA). The parameters of the proposed model are simulated under various 5G traffic conditions, including URLLC, eMBB, and mMTC. Performance streams are used with slice utilization, latency, throughput and energy efficiency being analyzed. The findings show that there was a 35 percent increase in resource utilization and a 27 percent decrease in the end-to-end latency ratio compared to the traditional methods that are heuristic-based. This will provide the foundations of what an AI-native 6G network will be.