Large Language Model Agents for Radio Map Generation and Wireless Network Planning

TL;DR

This paper introduces an automated approach using large language model agents to generate radio maps and assist in wireless network planning, significantly reducing manual effort and improving network coverage and quality in urban areas.

Contribution

The paper presents a novel LLM-based agent system that automates radio map creation and network planning, addressing scalability and user-friendliness issues in existing methods.

Findings

Manual operations are significantly reduced.

Enhanced coverage and SINR in urban environments.

Validated effectiveness through experimental results.

Abstract

Using commercial software for radio map generation and wireless network planning often require complex manual operations, posing significant challenges in terms of scalability, adaptability, and user-friendliness, due to heavy manual operations. To address these issues, we propose an automated solution that employs large language model (LLM) agents. These agents are designed to autonomously generate radio maps and facilitate wireless network planning for specified areas, thereby minimizing the necessity for extensive manual intervention. To validate the effectiveness of our proposed solution, we develop a software platform that integrates LLM agents. Experimental results demonstrate that a large amount manual operations can be saved via the proposed LLM agent, and the automated solutions can achieve an enhanced coverage and signal-to-interference-noise ratio (SINR), especially in urban environments.