Intent-Based Network for RAN Management with Large Language Models

2025-07-17

AI Summary

To address the growing complexity of wireless access network (RAN) management and the inefficiency of manual configuration, this paper proposes a large language model (LLM)-driven intent-based RAN autonomy framework. Built upon an agent architecture, the framework integrates structured prompt engineering with a closed-loop control mechanism to enable end-to-end automatic translation of high-level network intents—e.g., “reduce energy consumption” or “guarantee edge-user QoS”—into executable RAN parameter configurations, real-time state inference, and dynamic optimization. Its key innovation lies in the first deep integration of LLMs into the RAN closed-loop control loop, enabling semantic-level intent understanding, multi-step reasoning, and adaptive resource scheduling. Experimental evaluation on live base stations demonstrates autonomous optimization of critical parameters, achieving an average 23.7% improvement in energy efficiency and significantly enhancing network autonomy and robustness.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Enhance intent translation for RAN management using LLMs
• Automate dynamic optimization of RAN energy efficiency
• Enable robust resource management via LLM-orchestrated systems

Innovation

• Leveraging LLMs for RAN intent translation
• Structured prompt engineering for automation
• LLM-orchestrated closed-loop energy optimization