Intent-Based Network for RAN Management with Large Language Models

• URL: http://arxiv.org/abs/2507.14230v2
• Date: Mon, 04 Aug 2025 05:31:16 GMT
• Title: Intent-Based Network for RAN Management with Large Language Models
• Authors: Fransiscus Asisi Bimo, Maria Amparo Canaveras Galdon, Chun-Kai Lai, Ray-Guang Cheng, Edwin K. P. Chong,
• Abstract summary: This paper proposes a novel automation approach for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs)<n>The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN.<n>It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.
• Score: 1.5588799679661638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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