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，即分析和预测特定区域内的射频信号强度与分布的过程，对蜂窝网络规划与部署至关重要。传统的射频信号映射方法依赖于基于测量数据构建的统计模型，虽然复杂度低但准确性不足；或采用射线追踪工具，虽能提升目标区域的精度，却面临计算复杂度增加的问题。近年来，机器学习（ML）作为数据驱动的射频信号传播建模方法兴起，通过利用合成数据集训练的模型在“未见”区域执行射频信号映射。本文提出Geo2SigMap——一种基于ML的高效高保真射频信号映射框架，其核心依托地理数据库。首先，我们开发了一套自动化框架，无缝集成三种开源工具：OpenStreetMap（地理数据库）、Blender（计算机图形学）与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，我们提出级联U-Net模型，该模型在合成数据集上进行预训练，并利用环境信息与稀疏测量数据生成精细的射频信号地图。最后，通过实际测量活动评估Geo2SigMap性能：三种用户设备（UE）收集了来自公民宽带无线服务（CBRS）频段内六个LTE小区的超过45,000个蜂窝信息数据点。结果表明，Geo2SigMap在预测UE参考信号接收功率（RSRP）时，平均均方根误差（RMSE）为6.04 dB，较现有方法平均提升3.59 dB。