Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes
Thomas Benton Townsend Dimitrios Michael Manias
作者信息
Abstract
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.引用本文复制引用
Thomas Benton Townsend,Dimitrios Michael Manias.Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes[EB/OL].(2026-08-23)[2026-09-01].https://arxiv.org/abs/2608.22644.学科分类
通信/无线通信
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