Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Autor:	Hoydis, Jakob, Cammerer, Sebastian, Aoudia, Fayçal Ait, Vem, Avinash, Binder, Nikolaus, Marcus, Guillermo, Keller, Alexander
Rok vydání:	2022
Předmět:	Computer Science - Information Theory Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 1 figure, 4 code listings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.11854 Zobrazit plný text záznamu View this record from Arxiv