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

Effective communication and sensing in next-generation wireless technologies require resource-intensive site surveys for data collection. These surveys capture key RF characteristics at various positions, including the Received Signal Strength Indicator (RSSI) and spatial spectrum (RSSI measured from all directions around the receiver). An alternative approach is Radio-Frequency (RF) signal spatial propagation modeling, which predicts received signals based on transceiver positions. Existing Neural Radiance Field (NeRF)-based methods exhibit a fundamental trade-off between scalability and fidelity. To address this challenge, we explore leveraging 3D Gaussian Splatting, an advanced technique for real-time image synthesis of 3D scenes from arbitrary camera poses. This work develops RFSPM, an end-to-end 3D Gaussian distribution-based framework for scalable RF signal Spatial Propagation Modeling. We evaluate RFSPM in spatial spectrum synthesis, demonstrating its learning efficiency compared to NeRF-based methods.