Learning Radio Environments by Differentiable Ray Tracing
Jakob Hoydis Fayçal Aït Aoudia Sebastian Cammerer Florian Euchner Merlin Nimier-David Stephan ten Brink Alexander Keller
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.