Cross-Band RF Radiance Field Modeling via 3D Gaussian Splatting and Propagation Mixture-of-Experts

Abstract

As multi-protocol radio-frequency (RF) environments (e.g., RFID, Wi-Fi, and 5G) become increasingly prevalent, practical RF modeling is expected to overcome single-band limitations and achieve zero-shot generalization across a broad spectrum. This requires a unified representation of frequency-dependent electromagnetic (EM) propagation rather than modeling each band separately. In this paper, we present XBandGS, a unified cross-band RF radiance field modeling framework built on 3D Gaussian Splatting (3DGS). Unlike prior 3DGS-based RF modeling methods restricted to single-frequency fitting, XBandGS introduces an EM propagation Mixture-of-Experts (MoE) that disentangles and learns the frequency-specific behaviors of different physics-guided propagation modes within a given 3D environment. In addition, our approach enables the effective use of non-co-located datasets measured at various locations for different frequency bands. Trained on sparse frequencies, XBandGS can predict RF radiance fields for arbitrary unseen frequencies across a broad spectrum. We evaluate the cross-band RF Power Angular Spectrum (PAS) reconstruction capability of XBandGS across diverse 3D environments, including 7 simulated indoor scenes spanning 1–94 GHz and 6 real-world scenes (4 indoor and 2 outdoor) spanning 0.7–5.8 GHz. Results show that XBandGS achieves high-fidelity PAS reconstruction in unseen frequency bands, with an average SSIM of 0.822 on simulated traces and 0.650 on real-world datasets.