用大模型打造能自主决策的无线网络智能体
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
- 基于大语言模型构建可自主决策的网络管理智能体
- 在切片资源分配中准确理解用户意图并保持性能最优
- 适合6G网络复杂场景下的智能运维与自动化管理
无线网络正面临规模与复杂性不断增长带来的挑战，亟需先进的人工智能驱动策略，尤其在即将到来的6G网络中。本文提出WirelessAgent，一种利用大语言模型（LLMs）构建能够执行复杂任务的AI智能体的新方法。该方法通过高级推理、多模态数据处理和自主决策有效提升网络性能。实验结果表明，WirelessAgent能够准确理解用户意图，高效分配网络切片资源，并持续维持最优性能。本文还定义了WirelessAgent的通用框架，包含其核心组件与智能体设计原则。
原文摘要 · Abstract (English)
Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.
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