Learning Radio Environments by Differentiable Ray Tracing
This addresses the need for accurate environment-specific channel modeling in 6G research, though it is incremental as it builds on existing differentiable ray tracing techniques.
The paper tackles the problem of calibrating material properties for ray tracing in 6G channel modeling by introducing a gradient-based method that integrates with differentiable ray tracers, validated on synthetic and real-world indoor MIMO measurements.
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.