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This research demonstrates the practical integration of Large Language Models into 5G core networks, enabling operators to manage dynamic routing via natural language prompts rather than traditional configuration commands. The study validates that semantic routers can accurately extract intent details from user inputs, bridging the gap between high-level operational goals and low-level network execution without relying on static route selection alone.
Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes
arXiv:2608.22644v1 Announce Type: cross 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.
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.
Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Systems and Control (eess.SY)
arXiv:2608.22644 [cs.NI]
(or arXiv:2608.22644v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2608.22644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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