Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene geometries is now relatively straightforward, determining material characteristics requires precise calibration using channel measurements. We therefore introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. Our method seamlessly integrates with differentiable ray tracers that enable the computation of derivatives of CIRs with respect to these parameters. Essentially, we approach field computation as a large computational graph wherein parameters are trainable akin to weights of a neural network (NN). We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.

翻译：射线追踪（RT）在6G研究中至关重要，用于生成空间一致且环境特定的信道冲激响应（CIRs）。尽管获取精确的场景几何结构现已相对直接，但确定材料特性仍需利用信道测量进行精细校准。为此，我们提出了一种新颖的基于梯度的校准方法，并辅以材料属性、散射和天线方向图的可微分参数化。该方法可直接与可微分射线追踪器集成，该追踪器能够计算CIRs关于这些参数的导数。本质上，我们将场计算视为一个大型计算图，其中参数如同神经网络（NN）的权重一样可训练。我们利用合成数据和真实室内信道测量数据（采用分布式多输入多输出（MIMO）信道探测仪）验证了所提方法的有效性。