Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
This work addresses the need for efficient and differentiable simulation tools in 6G research, such as for reconfigurable intelligent surfaces and digital twins, representing a novel method for a known bottleneck.
The paper tackles the challenge of simulating radio wave propagation by introducing Sionna RT, a differentiable ray tracing library integrated into TensorFlow, which enables gradient computation for system and environment parameters, with applications like learning radio materials and optimizing transmitter orientations.
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation. This unique feature allows for the computation of gradients of the channel impulse response and other related quantities with respect to many system and environment parameters, such as material properties, antenna patterns, array geometries, as well as transmitter and receiver orientations and positions. In this paper, we outline the key components of Sionna RT and showcase example applications such as learning radio materials and optimizing transmitter orientations by gradient descent. While classic ray tracing is a crucial tool for 6G research topics like reconfigurable intelligent surfaces, integrated sensing and communications, as well as user localization, differentiable ray tracing is a key enabler for many novel and exciting research directions, for example, digital twins.