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

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.

Authors

Institutions

Publication Details

Journal
IEEE Networking Letters
Published
2025-02-07
DOI
https://doi.org/10.1109/lnet.2025.3539829
Citations
9
Primary Topic
Speech and dialogue systems
Type
article
Field-Weighted Citation Impact
22.33

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Hongping Quan, Wanli Ni, Ziyi Xie, Hui Song et al.
9 citations
IEEE Networking Letters
Speech and dialogue systems
22.33
article

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

Hongping Quan, Wanli Ni, Ziyi Xie, Hui Song, Tong Zhang, Yuanwei Liu, Xiangyu Ye, Shuai Wang
article en
9 citations

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.

IEEE Networking LettersVol. 7(3)
Harbin Institute of Technology (CN), UK Power Networks (GB), Shenzhen Institutes of Advanced Technology (CN), University of Hong Kong (HK), Tsinghua University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Speech and dialogue systems
22.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.