Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

[2608.14599] Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Networking and Internet Architecture arXiv:2608.14599 (cs) [Submitted on 30 Jun 2026 ( v1 ), last revised 15 Sep 2026 (this version, v2)] Title: Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach Authors: Zhenyu Tao , Yuxuan Li , Wei Xu , Yongming Huang , Xiaohu You View a PDF of the paper titled Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach, by Zhenyu Tao and 4 other authors View PDF HTML (experimental) Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging due to its dependency on site-specific radio propagation and user spatial distributions, both of which are unfortunately difficult to obtain prior to deployment. To overcome this barrier, we propose an intelligent BS deployment framework that integrates a geographic data-informed wireless network digital twin (DT) with deep reinforcement learning (DRL), enabling sample-free macro BS deployment optimization from solely open geographic data, without on-site measurements, real user trajectories, or exhaustive ray tracing. The proposed DT incorporates a sample-free radio map prediction model with hybrid input representation to achieve kilometer-scale signal strength estimation in milliseconds, complemented by a diffusion-based generative model for trajectory synthesis to collectively characterize channel and user distributions. Leveraging the DT as a virtual training environment, we formulate BS deployment as a multi-step Markov decision process (MDP) and solve it via a spatially structured DRL algorithm. A local search process and a Wasserstein distance-based deployment buffer are further incorporated to efficiently explore the large combinatorial solution space. Experimental results in real-world urban scenarios demonstrate that the geographic data-informed DT attains accuracy comparable to 100-sample-based prediction, and the intelligent BS deployment framework achieves up to 98.9% of the idealized benchmark performance while reducing optimization overhead by over 99%. Subjects: Networking and Internet Architecture (cs.NI) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14599 [cs.NI] (or arXiv:2608.14599v2 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2608.14599 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhenyu Tao [ view email ] [v1] Tue, 30 Jun 2026 08:20:05 UTC (4,968 KB) [v2] Tue, 15 Sep 2026 12:11:27 UTC (5,968 KB) Full-text links: Access Paper: View a PDF of the paper titled Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach, by Zhenyu Tao and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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