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

arXiv: https://arxiv.org/abs/2203.11854 · HTML: https://mlsanity.com/paper/2203.11854/

## Key facts

- Cited by 145 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications](https://mlsanity.com/paper/1911.06257/) (2019)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade](https://mlsanity.com/paper/2112.13187/) (2021)
- [What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G](https://mlsanity.com/paper/2105.04842/) (2021)
- [When Wireless Communications Meet Computer Vision in Beyond 5G](https://mlsanity.com/paper/2010.06188/) (2020)
- [Digital Twin Network: Opportunities and Challenges](https://mlsanity.com/paper/2201.01144/) (2022)
- [Versatile Mobile Communications Simulation: The Vienna 5G Link Level Simulator](https://mlsanity.com/paper/1806.03929/) (2018)

## Built on by (most cited first, 30 of 145)

- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Wireless Deep Video Semantic Transmission](https://mlsanity.com/paper/2205.13129/) (2022)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Forking Uncertainties: Reliable Prediction and Model Predictive Control with Sequence Models via Conformal Risk Control](https://mlsanity.com/paper/2310.10299/) (2023)
- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://mlsanity.com/paper/2412.06861/) (2024)
- [Graph Neural Networks for Channel Decoding](https://mlsanity.com/paper/2207.14742/) (2022)
- [Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications](https://mlsanity.com/paper/2310.19460/) (2023)
- [Radio Map Prediction from Aerial Images and Application to Coverage Optimization](https://mlsanity.com/paper/2410.17264/) (2024)
- [A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency](https://mlsanity.com/paper/2502.11965/) (2025)
- [Learning Linear Block Error Correction Codes](https://mlsanity.com/paper/2405.04050/) (2024)
- [Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding](https://mlsanity.com/paper/2211.04339/) (2022)
- [Score-based Source Separation with Applications to Digital Communication Signals](https://mlsanity.com/paper/2306.14411/) (2023)
- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Bit Error and Block Error Rate Training for ML-Assisted Communication](https://mlsanity.com/paper/2210.14103/) (2022)
- [Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings](https://mlsanity.com/paper/2405.13356/) (2024)
- [Differentiable and Learnable Wireless Simulation with Geometric Transformers](https://mlsanity.com/paper/2406.14995/) (2024)
- [A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments](https://mlsanity.com/paper/2303.06947/) (2023)
- [Integrating Pre-Trained Language Model with Physical Layer Communications](https://mlsanity.com/paper/2402.11656/) (2024)
- [Model-based learning for location-to-channel mapping](https://mlsanity.com/paper/2308.14370/) (2023)
- [Model-based Deep Learning for Beam Prediction based on a Channel Chart](https://mlsanity.com/paper/2312.02239/) (2023)
- [RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments](https://mlsanity.com/paper/2406.16907/) (2024)
- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [Deep Learning-Based Synchronization for Uplink NB-IoT](https://mlsanity.com/paper/2205.10805/) (2022)
- [CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital Twins](https://mlsanity.com/paper/2401.03310/) (2024)
- [Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding](https://mlsanity.com/paper/2406.12900/) (2024)
- [Model-based learning for multi-antenna multi-frequency location-to-channel mapping](https://mlsanity.com/paper/2407.07719/) (2024)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)

## Abstract (arXiv)

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.