HalifaxDT：基于开放地理数据的无线数字孪生

中文摘要 AI 辅助

无线数字孪生可以支持未来无线网络的站点特定分析、规划与实验。然而，在缺乏精确三维城市模型的情况下，构建城市规模的数字孪生仍具挑战性。本文提出HalifaxDT，一个基于加拿大新斯科舍省哈利法克斯半岛的异构开放地理空间与频谱数据构建的无线数字孪生。HalifaxDT通过一个工作流整合了建筑足迹、LiDAR高程产品、建筑元数据和频谱许可记录，该工作流在保留几何决策来源的同时，协调了多种建筑高度信息来源。生成的地形、建筑和网关元数据被集成到Sionna RT中进行无线仿真。我们通过两个用例评估HalifaxDT。第一个用例将其覆盖预测与基于现场测量得到的参考值及解析基线进行比较。第二个用例利用该数字孪生在正常操作条件下预测接收信号并检测干扰。覆盖结果表明，HalifaxDT比解析基线更好地保留了实测无线电地图的空间结构。干扰研究表明，与预测参考值的偏差可以揭示仅凭接收功率难以检测的干扰。我们还指出了当前的保真度限制，包括简化的材料表示、缺少植被，以及需要更广泛的射频校准和与实时测量的同步。

英文摘要

Wireless digital twins can support site-specific analysis, planning, and experimentation for future wireless networks. However, constructing them at city scale remains challenging when accurate 3D city models are unavailable. This paper presents HalifaxDT, a wireless digital twin of the Halifax Peninsula in Nova Scotia, Canada, constructed from heterogeneous open geospatial and spectrum data. HalifaxDT combines building footprints, LiDAR elevation products, building metadata, and spectrum licensing records through a workflow that reconciles multiple sources of building-height information while preserving the provenance of geometry decisions. The resulting terrain, buildings, and gateway metadata are integrated into Sionna RT for wireless simulation. We evaluate HalifaxDT through two use cases. The first compares its coverage predictions with a reference derived from field measurements and with an analytical baseline. The second uses the digital twin to predict the received signal under normal operating conditions and detect interference. The coverage results show that HalifaxDT better preserves the spatial structure of the measured radio map than the analytical baseline. The interference study shows that deviations from the predicted reference can reveal interference that is difficult to detect from received power alone. We also identify current fidelity limitations, including simplified material representation, missing vegetation, and the need for broader RF calibration and synchronization with live measurements.

发表机构

• Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机科学学院）
• LISN, Université Paris-Saclay（巴黎萨克雷大学 LISN）

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