GSRF: Complex-Valued 3D Gaussian Splatting for Radio-Frequency Signal Data Synthesis
Kang Yang, Gaofeng Dong, Sijie Ji, Wan Du, Mani Srivastava
Synthesizing Radio-Frequency (RF) signal data is essential for wireless sensing applications but remains challenging due to complex interactions, including reflection, diffraction, scattering, and phase-dependent interference. State-of-the-art Neural Radiance Field (NeRF)-based methods achieve high fidelity in RF data synthesis but suffer from long training times and high inference latency. We introduce GSRF, a framework that extends 3D Gaussian Splatting (3DGS) to the RF domain, enabling efficient, high-fidelity RF signal data synthesis. Unlike nanoscale visible light, RF signals with centimeter-scale wavelengths require phase-aware modeling to capture complex propagation effects. GSRF addresses these challenges with three innovations: first, complex-valued 3D Gaussians with a hybrid Fourier-Legendre basis to model directional and phase-dependent radiance; second, orthographic splatting for efficient ray-Gaussian intersection identification; and third, a complex-valued ray tracing algorithm executed on RF-customized CUDA kernels, grounded in wavefront propagation principles, to synthesize RF signal data in real time. Evaluated on data synthesis across various RF technologies, GSRF demonstrates significant improvements in training efficiency, training time, and inference latency.