Related papers: Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

URL: http://arxiv.org/abs/2303.11103v2
Date: Wed, 19 Jul 2023 14:42:10 GMT
Title: Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Authors: Jakob Hoydis, Fay\c{c}al A\"it Aoudia, Sebastian Cammerer, Merlin Nimier-David, Nikolaus Binder, Guillermo Marcus, Alexander Keller
Abstract summary: Sionna is a GPU-accelerated open-source library for link-level simulations based on. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation.
Score: 65.17711407805756
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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