GaussTwin: A Unified Scene Representation for Wireless Channel Prediction with Surface Recovery

Given channel measurements at a set of transmitter (Tx)-receiver (Rx) links, site-specific channel prediction aims to capture the effects of the propagation environment on angular and delay power distributions and received power at unmeasured Tx-Rx links. Existing methods either take scene geometry as prior information or learn radio representations that do not recover surfaces. We propose GaussTwin, a site-specific wireless channel model that represents the scene with oriented 2D Gaussian surface primitives, predicts channel characteristics at unseen links, and recovers explicit environment geometry from channel measurements. A physical propagation operator generates these characteristics for different Tx-Rx pairs from the primitives and models both specular reflection and diffuse scattering. To the best of our knowledge, GaussTwin is the first method that predicts channel characteristics and can recover explicit environment geometry from channel measurements at the same time without geometric supervision or prior information. Results across five indoor scenes and one outdoor city block show that GaussTwin achieves high-quality predictions while retaining strong surface recovery capability. On public datasets with real measurements, GaussTwin outperforms state-of-the-art baselines on spatial spectrum prediction and achieves comparable performance on received power prediction.