Automatic Network Planning with Digital Radio Twin
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908848063774720 |
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| author | Li, Xiaomeng Zhang, Yuru Liu, Qiang Vuran, Mehmet Can Huynh, Nathan Zhao, Li Rahman, Mizan Ozguven, Eren Erman |
| author_facet | Li, Xiaomeng Zhang, Yuru Liu, Qiang Vuran, Mehmet Can Huynh, Nathan Zhao, Li Rahman, Mizan Ozguven, Eren Erman |
| contents | Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose \emph{AutoPlan}, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-to-real discrepancy based on crowdsource real-world user data. Leveraging the DRT, we design a Bayesian optimization based algorithm to optimize the deployment parameters of base stations efficiently. Using the field measurement from Husker-Net, we extensively evaluate \emph{AutoPlan} under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that \emph{AutoPlan} flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2\% of its computation time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Network Planning with Digital Radio Twin Li, Xiaomeng Zhang, Yuru Liu, Qiang Vuran, Mehmet Can Huynh, Nathan Zhao, Li Rahman, Mizan Ozguven, Eren Erman Networking and Internet Architecture Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose \emph{AutoPlan}, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-to-real discrepancy based on crowdsource real-world user data. Leveraging the DRT, we design a Bayesian optimization based algorithm to optimize the deployment parameters of base stations efficiently. Using the field measurement from Husker-Net, we extensively evaluate \emph{AutoPlan} under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that \emph{AutoPlan} flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2\% of its computation time. |
| title | Automatic Network Planning with Digital Radio Twin |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.12441 |