Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model

TL;DR

This paper introduces a novel hierarchical GenAI-enabled intent-driven network management framework for 6G RAN, demonstrating significant performance improvements over existing approaches through a case study with the Mamba model.

Contribution

It proposes the first hierarchical GenAI-integrated IDN framework based on Mamba-SSM, enhancing automation and network performance in 6G RAN environments.

Findings

Reduces quality of service drift by up to 70%

Improves throughput by up to 80 Mbps

Lowers inference time to 60-70 ms

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, enabling adaptive and 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 selective State-Space Model (SSM)-enabled IDN architecture that integrates GenAI across three key stages: intent processing, intent validation, and intent execution. For the first time in the literature, we propose a hierarchical framework built on Mamba-SSM that introduces GenAI across all stages of the IDN pipeline. We further present a case study demonstrating that the proposed Mamba architecture significantly improves network performance through intelligent automation, surpassing existing IDN approaches. In a multi-cell 5G/6G scenario, the proposed architecture reduces quality of service drift by up to 70%, improves throughput by up to 80 Mbps, and lowers inference time to 60-70 ms, outperforming GenAI, reinforcement learning, and non-machine learning baselines.