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

TL;DR

SANDWICH is an offline, fully differentiable neural ray-tracing surrogate for 3D wireless channel modeling that outperforms existing online methods and is trainable entirely on GPUs.

Contribution

It introduces SANDWICH, a novel offline, differentiable framework for wireless ray-tracing that models environmental and signal properties without real-time supervision.

Findings

Outperforms baseline by 4e^-2 radian in RT accuracy

Fades only 0.5 dB from top-line channel gain estimation

Can be trained entirely on GPUs

Abstract

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 environmental supervision 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, leveraging generative models to jointly learn the optical, physical, and signal properties within each designated environment. Our work introduces the Scene-Aware Neural Decision Wireless Channel Raytracing Hierarchy (SANDWICH), an innovative offline, fully differentiable approach that can be trained entirely on GPUs. SANDWICH offers superior performance compared to existing online learning methods, outperforms the baseline by 4e^-2 radian in RT accuracy, and only fades 0.5 dB away from toplined channel gain estimation.