Sionna RT让无线传播模拟可微分，加速建模与优化。
Sionna RT: Technical Report
- 结合SBR与镜像法模拟射线传播，支持梯度计算。
- 相比旧版速度更快、内存更低，支持复杂环境建模。
- 适合通信系统优化与可微仿真研究者使用。
Sionna 是一个开源的 GPU 加速库，自 0.14 版本起引入了可微分的射线追踪模块 Sionna RT，用于模拟无线电波传播。其独特之处在于支持对信道冲激响应（CIR）、无线电图等指标关于系统与环境参数（如材料属性、天线方向图、阵列几何）的梯度计算。Sionna 1.0 的发布对射线追踪器进行了全面重构，显著提升了运行速度、内存效率与可扩展性。本文详细描述了 Sionna RT 用于高效模拟无线电波传播的算法，同时指出当前局限。由于计算 CIR 与无线电图需不同算法，分别在独立章节中详述：CIR 采用射线发射与弹跳（SBR）结合镜像法，并通过哈希机制高效去除重复路径；无线电图则采用纯 SBR 方法实现。
原文摘要 · Abstract (English)
Sionna is an open-source, GPU-accelerated library that, as of version 0.14, incorporates a ray tracer, Sionna RT, for simulating radio wave propagation. A unique feature of Sionna RT is differentiability, enabling the calculation of gradients for the channel impulse responses (CIRs), radio maps, and other related metrics with respect to system and environmental parameters, such as material properties, antenna patterns, and array geometries. The release of Sionna 1.0 provides a complete overhaul of the ray tracer, significantly improving its speed, memory efficiency, and extensibility. This document details the algorithms employed by Sionna RT to simulate radio wave propagation efficiently, while also addressing their current limitations. Given that the computation of CIRs and radio maps requires distinct algorithms, these are detailed in separate sections. For CIRs, Sionna RT integrates shooting and bouncing of rays (SBR) with the image method and uses a hashing-based mechanism to efficiently eliminate duplicate paths. Radio maps are computed using a purely SBR-based approach.
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