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

Hongye Quan, Wanli Ni, Tong Zhang, Xiangyu Ye, Ziyi Xie, Shuai Wang, Yuanwei Liu, Hui Song

TL;DR

The paper tackles the challenge of scalable, automated radio map generation and wireless network planning in dense urban environments, where traditional commercial tools require heavy manual operations. It introduces an LLM-agent framework with Profile, Tools, and Models modules, along with memory and task planning functionalities, to autonomously generate radio maps and optimize base station deployment. A PySide6/Python-based software platform integrates with external tools and RANPLAN ACADEMIC to enable end-to-end automation. Real-world experiments demonstrate substantial reductions in manual operations and improvements in coverage and SINR, particularly in urban settings, highlighting the practical potential for scalable automated radio-mapping and planning across diverse geographies.

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.

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

TL;DR

The paper tackles the challenge of scalable, automated radio map generation and wireless network planning in dense urban environments, where traditional commercial tools require heavy manual operations. It introduces an LLM-agent framework with Profile, Tools, and Models modules, along with memory and task planning functionalities, to autonomously generate radio maps and optimize base station deployment. A PySide6/Python-based software platform integrates with external tools and RANPLAN ACADEMIC to enable end-to-end automation. Real-world experiments demonstrate substantial reductions in manual operations and improvements in coverage and SINR, particularly in urban settings, highlighting the practical potential for scalable automated radio-mapping and planning across diverse geographies.

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.
Paper Structure (13 sections, 7 figures, 1 table)

This paper contains 13 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. The Proposed LLM Agent Framework
  3. The Module Design
  4. Profile Module
  5. Tool Module
  6. Model Module
  7. Functionality
  8. Memory Functionality
  9. Task Planning Functionality
  10. Software Platform and Experiment Results
  11. Software Platform Development
  12. Experiment Results
  13. Conclusions

Figures (7)

  • Figure 1: Proposed framework of LLM agent for radio map generation and wireless network planning.
  • Figure 2: Flow chart of the proposed software platform, where OSM file refers to OpenStreetMap file.
  • Figure 3: User interface of the proposed software platform.
  • Figure 4: Radio map generation and wireless network optimization tasks for HITSZ.
  • Figure 5: Radio map generation and wireless network optimization tasks for Hyde Park.
  • ...and 2 more figures