Intent-Based Network for RAN Management with Large Language Models

TL;DR

This paper introduces an innovative intent-based RAN management system utilizing Large Language Models to interpret high-level goals, optimize network configurations, and improve energy efficiency through autonomous, feedback-driven adjustments.

Contribution

It presents a novel integration of LLMs into RAN management, enabling autonomous intent translation and dynamic optimization within an agentic architecture.

Findings

Automated intent translation improves RAN configuration accuracy.

Dynamic optimization enhances energy efficiency in RAN.

LLM-based system adapts strategies based on real-time feedback.

Abstract

Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.