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.

翻译：无线射线追踪（RT）正逐渐成为三维（3D）无线信道建模的关键工具，这得益于图形渲染技术的进步。现有方法难以准确建模超5G（B5G）网络信号，这些信号通常工作在更高频段，且对环境条件和变化更为敏感。现有的在线学习解决方案在训练期间需要实时环境监控，这既成本高昂又与基于GPU的处理不兼容。为此，我们提出了一种新颖方法，将射线轨迹生成重新定义为序列决策问题，利用生成模型在指定环境中联合学习光学、物理和信号特性。我们的工作引入了场景感知神经决策无线信道射线追踪层次模型（SANDWICH），这是一种创新的离线、完全可微分方法，可以完全在GPU上进行训练。与现有在线学习方法相比，SANDWICH提供了卓越的性能，在RT精度上比基线高出4e^-2弧度，并且在信道增益估计方面仅比最优性能低0.5 dB。