Intent-Based 6G Management with Generative AI

Abstract

The 6G era introduces unprecedented complexity in managing heterogeneous, large-scale, and dynamic network infrastructures. These challenges are addressed by the concept of Intent-Based Networking (IBN), which has emerged as a promising paradigm for autonomous network management, enabling users to express high-level objectives that are automatically translated and enforced by the network. However, current IBN solutions remain constrained by rigid structured specifications and limited assurance mechanisms. This paper presents an overview of PhD research leveraging Generative AI (GenAI), specifically Large Language Models (LLMs), to address three fundamental IBN challenges: (i) intent translation, (ii) intent assurance, and (iii) GenAI operations in IBN systems. We propose a set of novel frameworks validated on real 5G/6G testbeds. Most contributions are supported by demos, datasets, and open-source implementations, which are referenced in the design section of each contribution. This PhD positions GenAI as a key enabler for advancing autonomous and user-centric 6G.