Abstract
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Its latest release (v0.14) 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.
Abstract (translated)
Sinomax是一个基于TensorFlow的链级模拟开源库,具有GPU加速功能。其最新版本(v0.14)集成了可区分的光线追踪(RT)功能,用于模拟无线电传播。这个独特特性允许对通道脉冲响应和其他相关量的计算梯度,与许多系统和环境参数,例如材料特性、天线模式、阵型几何学以及发射器和接收器的方向和位置进行计算。在本文中,我们概述了Sinomax RT的关键组件,并展示了示例应用,例如学习无线电材料和应用梯度下降优化发射器方向。虽然经典光线追踪对于像可重构智能表面、集成传感和通信以及用户定位等6G研究主题是至关重要的工具,但可区分的光线追踪为许多新颖和令人兴奋的研究方向,例如数字双胞胎等提供了关键 enabler。
URL
https://arxiv.org/abs/2303.11103
