发表机构
Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出采用带语义路由器的动态路由，结合LLM增强型意图驱动网络，实现5G+核心网络的意图识别与细节提取，实验显示静态和动态路由在细节提取及模式格式化上均表现良好。
AI 中文摘要
随着人工智能（AI）和大语言模型（LLMs）在日常应用中的使用日益普遍，其对自然语言的理解能力已显著提升。AI的一项新兴应用是与网络管理及编排实践相融合，LLM增强型意图驱动网络便是此类融合的一个实例，网络运营商可借助自然语言控制网络。本研究提出在意图驱动的5G+核心网络中，采用带有语义路由器的动态路由，以识别网络运营商提示中的意图，并提取实现该意图所需的必要细节。此外，本研究通过针对一系列真实运营商提示评估多种编码器，分析静态路由选择的性能，同时评估动态路由细节提取的准确性。所得结果表明，静态路由与动态路由在细节提取及模式格式化方面均表现良好。
英文摘要
As the use of Artificial Intelligence (AI) and Large Language Models (LLMs) is becoming common in everyday applications, their ability to interpret natural language has increased significantly. An emerging application of AI is integration with network management and orchestration practices. An instance of this integration is LLM-enhanced intent-based networking, where network operators will control a network using natural language. This work presents the use of dynamic routes with a semantic router to identify an intent from a network operator's prompt and extract necessary details for intent fulfillment in intent-based 5G+ core networks. Furthermore, the performance of static route selection is assessed by evaluating multiple encoders and dynamic route detail extraction accuracy against a series of realistic operator prompts. The presented results show that static and dynamic routes are successful in detail extraction and schema formatting.
CommentsAccepted at IEEE GLOBECOM 2026
