AI-Native Network Slicing for 6G Networks

TL;DR

This paper proposes an AI-native network slicing architecture for 6G networks, integrating AI for intelligent management and supporting AI-driven services, addressing future network complexities.

Contribution

It introduces a novel AI-native network slicing framework for 6G, combining AI-based management with slicing solutions tailored for AI services.

Findings

AI-based management improves network slice efficiency

Support for emerging AI services through optimized resource allocation

Case study demonstrates feasibility of AI-native slicing architecture

Abstract

With the global roll-out of the fifth generation (5G) networks, it is necessary to look beyond 5G and envision the 6G networks. The 6G networks are expected to have space-air-ground integrated networks, advanced network virtualization, and ubiquitous intelligence. This article presents an artificial intelligence (AI)-native network slicing architecture for 6G networks to enable the synergy of AI and network slicing, thereby facilitating intelligent network management and supporting emerging AI services. AI-based solutions are first discussed across network slicing lifecycle to intelligently manage network slices, i.e., AI for slicing. Then, network slicing solutions are studied to support emerging AI services by constructing AI instances and performing efficient resource management, i.e., slicing for AI. Finally, a case study is presented, followed by a discussion of open research issues that are essential for AI-native network slicing in 6G networks.