Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. It enables the rapid prototyping of complex communication system architectures and provides native support for the integration of neural networks. Sionna implements a wide breadth of carefully tested state-of-the-art algorithms that can be used for benchmarking and end-to-end performance evaluation. This allows researchers to focus on their research, making it more impactful and reproducible, while saving time implementing components outside their area of expertise. This white paper provides a brief introduction to Sionna, explains its design principles and features, as well as future extensions, such as integrated ray tracing and custom CUDA kernels. We believe that Sionna is a valuable tool for research on next-generation communication systems, such as 6G, and we welcome contributions from our community.
翻译:Sionna是一款基于TensorFlow的GPU加速开源库,用于链路层模拟。它支持快速原型设计复杂通信系统架构,并提供神经网络集成的本地支持。Sionna实现了广泛的、经过认真测试的最新算法,可用于基准测试和端到端性能评估。这使得研究人员可以专注于自己的研究,使其更具影响力和可重复性,同时节省在自己专业领域之外实现组件的时间。本白皮书简要介绍了Sionna,解释了它的设计原则和功能,以及未来的扩展,例如集成射线追踪和自定义CUDA核。我们相信,Sionna 是研究下一代通信系统(如6G)的有价值的工具,欢迎社区贡献。