用大模型驱动智能体，实现5G/6G网络自主控制
LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization
- 构建基于大模型的智能体系统框架，支持推理与规划
- 将智能体能力映射到5G/6G控制面，推动标准化落地
- 适合关注6G智能运维与协议融合的研究者
由大型语言模型驱动的智能体人工智能，正推动下一代网络（NGNs）从规则化自动化转向自主、目标导向的控制。现有综述多将该领域与通信网络分而论之，忽视了协议集成、评估方法及标准化对齐。为此，本文提出两部分教程与综述：第一部分形式化定义5G/6G的控制、管理与原生AI平面，阐述智能体的核心基础——推理、规划、工具调用、多智能体协作与评估机制；第二部分将智能体能力映射至5G/6G控制面、标准化进程及主要6G倡议，揭示未来自主电信网络的关键挑战。
原文摘要 · Abstract (English)
Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs). Existing surveys treat the two domains in isolation, leaving protocol integration, evaluation, and standardization alignment underexplored. To address this gap, a two-part tutorial-and-survey is presented. Part I formalises the control, management, and AI-native planes of 5G and 6G. It then covers the foundations of agentic systems: reasoning, planning, tool use, multi-agent coordination, and evaluation. Part II maps agentic capabilities onto 5G/6G control surfaces, standardization, and major 6G initiatives. Finally, it identifies open challenges shaping autonomous telecommunications.
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