用可微分的离线模型提升5G以上无线信道建模精度
SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate
- 将射线轨迹生成转为序列决策问题，用生成模型联合学习光、物理与信号特性
- 在射线追踪精度上比基线提升4e^-2弧度，信道增益估计仅差0.5 dB
- 完全离线训练，支持GPU加速，适合高频率无线系统仿真
无线射线追踪（RT）正成为三维无线信道建模的关键工具，得益于图形渲染技术的进步。现有方法难以准确建模高于5G（B5G）的网络信号，后者常工作于更高频段，且更易受环境条件与变化影响。现有在线学习方案需实时环境监督进行训练，成本高且不兼容基于GPU的处理。为此，我们提出新方法，将射线轨迹生成重新定义为序列决策问题，利用生成模型联合学习特定环境中的光学、物理和信号属性。本文引入场景感知神经决策无线信道射线追踪层级（SANDWICH），一种创新的离线、全可微方法，可在GPU上全程训练。SANDWICH性能优于现有在线学习方法，在射线追踪精度上比基线提升4×10⁻²弧度，信道增益估计仅落后顶级方法0.5 dB。
原文摘要 · Abstract (English)
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.
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