“Made with Sionna”

We love to see how Sionna is used by other researchers! For this reason, you find below links to papers whose authors have also published Sionna-based simulation code.

List of Projects

If you want your paper and code be listed here, please send an email to sionna@nvidia.com with links to the paper (e.g., arXiv) and code repository (e.g., GitHub).

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer, Merlin Nimier-David, Nikolaus Binder, Guillermo Marcus, Alexander Keller

Released in 2023 and based on Sionna v0.14.

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 of 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.

DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

Reinhard Wiesmayr, Chris Dick, Jakob Hoydis, Christoph Studer

Released in 2022 and based on Sionna v0.11.

Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates. DUIDD interleaves the inner stages of the data detector and channel decoder, which expedites convergence and reduces complexity. Furthermore, DUIDD applies deep unfolding to automatically optimize algorithmic hyperparameters, soft-information exchange, message damping, and state forwarding. We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity.

Bit Error and Block Error Rate Training for ML-Assisted Communication

Bit Error and Block Error Rate Training for ML-Assisted Communication

Reinhard Wiesmayr, Gian Marti, Chris Dick, Haochuan Song, Christoph Studer

Released in 2022 and based on Sionna v0.11.

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.

GNNs for Channel Decoding

Graph Neural Networks for Channel Decoding

Sebastian Cammerer, Jakob Hoydis, Fayçal Aït Aoudia, Alexander Keller

Released in 2022 and based on Sionna v0.11.

We propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph that represents the forward error correction code structure by replacing node and edge message updates with trainable functions.

DL-based Synchronization of NB-IoT

Deep Learning-Based Synchronization for Uplink NB-IoT

Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Matthijs Van Keirsbilck, Alexander Keller

Released in 2022 and based on Sionna v0.11.

We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.