Scalable 3D Gaussian Splatting-Based RF Signal Spatial Propagation Modeling

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\,$\times$, 18.6\,$\times$, and 84.4\,$\times$, respectively.