Large Language Model Agents for Radio Map Generation and Wireless Network Planning
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916611441557504 |
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| author | Quan, Hongye Ni, Wanli Zhang, Tong Ye, Xiangyu Xie, Ziyi Wang, Shuai Liu, Yuanwei Song, Hui |
| author_facet | Quan, Hongye Ni, Wanli Zhang, Tong Ye, Xiangyu Xie, Ziyi Wang, Shuai Liu, Yuanwei Song, Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model Agents for Radio Map Generation and Wireless Network Planning Quan, Hongye Ni, Wanli Zhang, Tong Ye, Xiangyu Xie, Ziyi Wang, Shuai Liu, Yuanwei Song, Hui Information Theory 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. |
| title | Large Language Model Agents for Radio Map Generation and Wireless Network Planning |
| topic | Information Theory |
| url | https://arxiv.org/abs/2501.11283 |