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.

Sionna是一个GPU加速的开源库，利用可微分的射线追踪器模拟无线电波传播，可以计算与物质特性、天线图案、阵列几何形状以及发射机和接收机方向和位置相关的通道脉冲响应梯度，提高6G研究方向的效率与精度如重新配置智能表面、整合感应与通信、用户定位及数字孪生等，是一个关键催化剂。

Sionna RT: 射线追踪在射频传播建模中的可微分应用