Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration

TL;DR

This paper introduces semantic routing to improve the accuracy and efficiency of LLM-assisted intent-based management in 5G core networks, through an end-to-end intent extraction framework and analysis of system components.

Contribution

It presents a novel semantic routing approach for intent-based network management, including a comprehensive intent extraction framework and analysis of encoder and quantization effects.

Findings

Semantic routing improves LLM accuracy in intent recognition.

Enhanced efficiency in network management tasks.

Analysis of encoder and quantization impacts on performance.

Abstract

Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications, especially in the fields of natural language processing and generative AI. Not limited to text generation applications, these models inherently possess the opportunity to leverage prompt engineering, where the inputs of such models can be appropriately structured to articulate a model's purpose explicitly. A prominent example of this is intent-based networking, an emerging approach for automating and maintaining network operations and management. This paper presents semantic routing to achieve enhanced performance in LLM-assisted intent-based management and orchestration of 5G core networks. This work establishes an end-to-end intent extraction framework and presents a diverse dataset of sample user intents accompanied by a thorough analysis of the effects of encoders and quantization on overall system performance. The results show that using a semantic router improves the accuracy and efficiency of the LLM deployment compared to stand-alone LLMs with prompting architectures.