用大模型实现无线网络意图管理，自动优化配置提升能效
Intent-Based Network for RAN Management with Large Language Models
- 通过智能体架构融合大模型，自动解析高层目标
- 闭环机制动态优化关键参数，提升网络能效
- 适合5G/6G网络自动化管理场景的从业者
无线网络管理复杂度持续上升，智能化自动化成为关键。本文提出一种基于大语言模型（LLMs）的无线接入网（RAN）意图网络自动化方法。通过构建智能体架构，集成大模型实现高阶目标的自动解析、复杂网络状态推理及精准配置生成。设计结构化提示工程策略，验证系统可通过闭环机制动态调整关键RAN参数，显著提升能效。结果表明，该方法能基于实时反馈自适应调整策略，为RAN提供稳健的资源管理能力。
原文摘要 · Abstract (English)
Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.
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