Intent-Based Network for RAN Management with Large Language Models
This addresses network management complexity for wireless operators, representing a novel application of LLMs to a domain-specific bottleneck.
The paper tackles the problem of managing complex Radio Access Networks (RANs) by proposing an intent-based automation approach using Large Language Models (LLMs), resulting in automatic energy efficiency improvements through dynamic optimization of RAN parameters.
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.