Generative AI for Intent-Driven Network Management in 6G: A Case Study on Hierarchical Learning Approach

2025-08-08

AI Summary

To address insufficient automation in Intent-Driven Networking (IDN) for heterogeneous, dynamic 6G networks, this paper proposes the first generative-AI–enabled IDN management architecture covering the full intent lifecycle—understanding, validation, and execution. Unlike prior works that integrate large language models (LLMs) only at isolated stages, our approach introduces an end-to-end hierarchical learning framework that synergistically combines LLMs with a state-aware Mamba architecture and hierarchical reinforcement learning to enable autonomous policy generation, semantic-consistency verification, and closed-loop execution. Experimental evaluation demonstrates substantial improvements in complex intent response accuracy and network adaptability, outperforming conventional IDN solutions across key metrics—including latency, throughput, and policy compliance—while ensuring robust intent interpretation and operational reliability in dynamic 6G environments.

Technology Category

• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (IDNs) address this by translating high-level intents into optimization policies. Large Language Models (LLMs) can enhance this process by understanding complex human instructions to enable adaptive, intelligent automation. Given the rapid advancements in Generative AI (GenAI), a comprehensive survey of LLM-based IDN architectures in disaggregated Radio Access Network (RAN) environments is both timely and critical. This article provides such a survey, along with a case study on a hierarchical learning-enabled IDN architecture that integrates GenAI across three key stages: intent processing, intent validation, and intent execution. Unlike most existing approaches that apply GenAI in the form of LLMs for intent processing only, we propose a hierarchical framework that introduces GenAI across all three stages of IDN. To demonstrate the effectiveness of the proposed IDN management architecture, we present a case study based on the latest GenAI architecture named Mamba. The case study shows how the proposed GenAI-driven architecture enhances network performance through intelligent automation, surpassing the performance of the conventional IDN architectures.

Problem

• Enhancing 6G network management with Generative AI
• Integrating GenAI across intent processing, validation, execution
• Improving automation in heterogeneous, dynamic 6G networks

Innovation

• Hierarchical learning integrates GenAI across IDN stages
• LLMs enhance intent processing with human-like understanding
• Mamba architecture demonstrates superior network performance