Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack accuracy, or ray tracing tools, which provide enhanced precision for the target area but suffer from increased computational complexity. Recently, machine learning (ML) has emerged as a data-driven method for modeling RF signal propagation, which leverages models trained on synthetic datasets to perform RF signal mapping in "unseen" areas. In this paper, we present Geo2SigMap, an ML-based framework for efficient and high-fidelity RF signal mapping using geographic databases. First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models. Second, we propose a cascaded U-Net model, which is pre-trained on synthetic datasets and employed to generate detailed RF signal maps, leveraging environmental information and sparse measurement data. Finally, we evaluate the performance of Geo2SigMap via a real-world measurement campaign, where three types of user equipment (UE) collect over 45,000 data points related to cellular information from six LTE cells operating in the citizens broadband radio service (CBRS) band. Our results show that Geo2SigMap achieves an average root-mean-square-error (RMSE) of 6.04 dB for predicting the reference signal received power (RSRP) at the UE, representing an average RMSE improvement of 3.59 dB compared to existing methods.

翻译：射频信号映射是分析和预测特定区域内射频信号强度与分布的过程，对于蜂窝网络规划与部署至关重要。传统射频信号映射方法依赖于基于测量数据构建的统计模型（复杂度低但精度不足）或射线追踪工具（目标区域精度提升但计算复杂度增加）。近年来，机器学习作为一种数据驱动方法涌现，通过训练合成数据集上的模型在"未知"区域执行射频信号映射。本文提出Geo2SigMap——一种基于机器学习的高效高保真射频信号映射框架，利用地理数据库实现。首先，我们开发了一个自动化框架，无缝整合三个开源工具：OpenStreetMap（地理数据库）、Blender（计算机图形学）和Sionna（射线追踪），实现大规模三维建筑地图与射线追踪模型的高效生成。其次，提出级联U-Net模型，该模型在合成数据集上预训练，用于利用环境信息与稀疏测量数据生成精细射频信号图。最后，通过真实世界测量活动评估Geo2SigMap性能：三种用户设备（UE）在公民宽带无线电服务（CBRS）频段内收集超过45,000个与六个LTE小区相关的蜂窝信息数据点。结果表明，Geo2SigMap对UE参考信号接收功率（RSRP）的预测平均均方根误差（RMSE）为6.04 dB，相比现有方法平均RMSE提升3.59 dB。