Knowledge-Enabled Intent-Driven Network Configuration Generation for 5G Core Networks

Automating the configuration of 5G core networks (CNs) is crucial for meeting increasingly diverse and specialized application requirements. In this paper, we propose an intent-driven automation model for configuring 5G CNs. Our approach leverages existing 5G CN equipment manuals and extracts the underlying configuration knowledge to enable intent translation. Unlike traditional methods, the proposed model employs a knowledge fusion approach that computes the semantic correlation between user intents and equipment configuration commands. Specifically, the proposed model integrates graph neural networks and pre-trained language models, exhibiting strong reasoning capabilities. Moreover, by converting complex texts into structured knowledge graphs, the proposed model provides precise command suggestions, significantly enhancing the degree of automation in 5G CN configurations. Simulation results show that the generated commands achieve high accuracy across various scenarios, validating the effectiveness of our approach.