{"ID":2893339,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14230","arxiv_id":"2507.14230","title":"Intent-Based Network for RAN Management with Large Language Models","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.","short_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 i...","url_abs":"https://arxiv.org/abs/2507.14230","url_pdf":"https://arxiv.org/pdf/2507.14230v2","authors":"[\"Fransiscus Asisi Bimo\",\"Maria Amparo Canaveras Galdon\",\"Chun-Kai Lai\",\"Ray-Guang Cheng\",\"Edwin K. P. Chong\"]","published":"2025-07-17T04:57:55Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}