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.

翻译：随着6G的出现，移动网络日益呈现异构化和动态化特征，亟需先进的自动化技术以实现高效管理。意图驱动网络通过将高层级意图转化为优化策略来解决这一问题。大型语言模型能够理解复杂的人类指令，从而增强这一过程，实现自适应和智能化的自动化。鉴于生成式人工智能的快速发展，对基于LLM的意图驱动网络架构在解耦无线接入网环境中的应用进行全面综述既及时又至关重要。本文提供了这样一篇综述，并辅以一个案例研究，展示了一种基于选择性状态空间模型的意图驱动网络架构，该架构将生成式人工智能整合到意图处理、意图验证和意图执行三个关键阶段。我们在文献中首次提出了一种基于Mamba-SSM的分层框架，该框架在意图驱动网络流程的所有阶段引入了生成式人工智能。我们进一步通过案例研究证明，所提出的Mamba架构通过智能自动化显著提升了网络性能，超越了现有的意图驱动网络方案。在多小区5G/6G场景中，该架构将服务质量漂移降低了高达70%，吞吐量提升了高达80 Mbps，并将推理时间缩短至60-70毫秒，其性能优于生成式人工智能、强化学习以及非机器学习基线方法。