Learning Frequency-Transferable Radio Digital Twins with Gaussian Ray Tracing

Emerging multi-band systems (e.g. WiFi 7) raise the importance of the multi-band radio-frequency digital twin (RFDT), a virtual replica of how radio signals propagate within a physical site, which serves numerous applications. However, existing approaches only focus on a single frequency band, and directly predicting the signal of unseen bands with these DTs does not work because RF propagation varies with frequency. Building an RFDT with multi-band data, on the other hand, incurs huge measurement overhead as the number of bands increases. We therefore propose FresGRT, the first one-to-many-band RFDT built on single-band RF measurements with 3D Gaussian ray tracing. FresGRT first learns the frequency-invariant representation based on the Fresnel equations to achieve cross-band prediction with only single-band data. FresGRT then learns from the frequency-selective fading to decouple the physically coupled representation parameters, and optimizes them separately with a staged training pipeline. We extensively evaluate FresGRT on real testbed and ray-tracing datasets. Results on the testbed show that FresGRT achieves unseen band prediction with a mean error of 4.4 dB, a 45% improvement over the best baseline, while reducing the survey time by 55%.