Effective network planning and sensing in wireless networks require resource-intensive site surveys for data collection. An alternative is Radio-Frequency (RF) signal spatial propagation modeling, which computes received signals given transceiver positions in a scene (e.g.s a conference room). We identify a fundamental trade-off between scalability and fidelity in the state-of-the-art method. To address this issue, we explore leveraging 3D Gaussian Splatting (3DGS), an advanced technique for the image synthesis of 3D scenes in real-time from arbitrary camera poses. By integrating domain-specific insights, we design three components for adapting 3DGS to the RF domain, including Gaussian-based RF scene representation, gradient-guided RF attribute learning, and RF-customized CUDA for ray tracing. Building on them, we develop RFSPM, an end-to-end framework for scalable RF signal Spatial Propagation Modeling. We evaluate RFSPM in four field studies and two applications across RFID, BLE, LoRa, and 5G, covering diverse frequencies, antennas, signals, and scenes. The results show that RFSPM matches the fidelity of the state-of-the-art method while reducing data requirements, training GPU-hours, and inference latency by up to 9.8×, 18.6×, and 84.4×, respectively.
有效的无线网络规划和感知需要资源密集型的现场调查来收集数据。另一种替代方法是射频（RF）信号空间传播建模，该方法根据场景中发射机和接收机的位置计算接收到的信号（例如会议室）。我们发现现有方法在可扩展性和保真度之间存在一个基本的权衡。为了解决这个问题，我们探索了利用3D高斯溅射（3DGS），一种用于实时合成3D场景图像的先进技术，可从任意相机位置生成图像。通过结合领域特定的见解，我们设计了三个组件，将3DGS适配到射频领域，包括基于高斯的射频场景表示、梯度引导的射频属性学习和射频定制CUDA用于光线追踪。在此基础上，我们开发了RFSPM，一个端到端的可扩展射频信号空间传播建模框架。我们在四个实地研究和两个应用中评估了RFSPM，涵盖了RFID、BLE、LoRa和5G，涉及不同的频率、天线、信号和场景。结果表明，RFSPM在保真度上与现有方法相当，同时将数据需求、训练GPU小时数和推理延迟分别减少了多达9.8倍、18.6倍和84.4倍。