SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate

Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to accurately model beyond 5G (B5G) network signaling, which often operates at higher frequencies and is more susceptible to environmental conditions and changes. Existing online learning solutions require real-time interaction with radio environment during training, which is both costly and incompatible with GPU-based processing. In response, we propose a novel approach that redefines ray trajectory generation as a sequential decision-making problem, solved with the proposed Scene-Aware Neural Decision Wireless Channel Raytracing Hierarchy (SANDWICH) approach. The SANDWICH approach leverages a decision transformer to jointly learn the optical, physical, and signal properties within each designated environment in a fully differentiable approach, which can be trained entirely on GPUs. SANDWICH offers superior performance compared to existing online learning methods, and outperforms the baseline by 4e<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> rad in RT accuracy. Furthermore, channel gain estimation w.r.t predicted trajectory only fades 0.5 dB away from using ground truth wireless RT result for channel gain estimation.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>