105 citations · 141 across the 14 of their papers we have counts for
23 papers
Ballistic transport and crystallographic control in altermagnetic quantum rings
Edilberto O. Silva
We study coherent two-terminal transport through a finite-width -wave altermagnetic quantum ring with nonmagnetic leads. A confined Aharonov--Bohm flux remains exactly per…
Autonomous Discovery of Wireless Communications Algorithms
Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer +4
Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields. Yet its appl…
Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN
Sebastian Cammerer, Guillermo Marcus, Tobias Zirr +4
We introduce the NVIDIA Sionna Research Kit, a GPU-accelerated research platform for developing and testing AI/ML algorithms in 5G NR cellular networks. Powered by the NVIDIA Jetso…
Design of a Standard-Compliant Real-Time Neural Receiver for 5G NR
Reinhard Wiesmayr, Sebastian Cammerer, Fayçal Aït Aoudia +3
We detail the steps required to deploy a multi-user multiple-input multiple-output (MU-MIMO) neural receiver (NRX) in an actual cellular communication system. This raises several e…
A Neural Receiver for 5G NR Multi-user MIMO
Sebastian Cammerer, Fayçal Aït Aoudia, Jakob Hoydis +4
We introduce a neural network (NN)-based multiuser multiple-input multiple-output (MU-MIMO) receiver with 5G New Radio (5G NR) physical uplink shared channel (PUSCH) compatibility.…
Learning Radio Environments by Differentiable Ray Tracing
Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer +4
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene…
Graph Neural Networks for Channel Decoding
Sebastian Cammerer, Jakob Hoydis, Fayçal Aït Aoudia +1
In this work, we propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase a competitive decoding performance for various codin…
Deep Learning-Based Synchronization for Uplink NB-IoT
Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer +2
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-acc…
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Jakob Hoydis, Sebastian Cammerer, Fayçal Ait Aoudia +4
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 p…
Convolutional Self-Attention-Based Multi-User MIMO Demapper
Athur Michon, Fayçal Ait Aoudia, K. Pavan Srinath
In orthogonal frequency division multiplexing (OFDM)-based wireless communication systems, the bit error rate (BER) performance is heavily dependent on the accuracy of channel esti…
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
Fayçal Ait Aoudia, Jakob Hoydis
As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that c…
Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single proc…
End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a p…
Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning
Fayçal Ait Aoudia, Jakob Hoydis
Orthogonal frequency division multiplexing (OFDM) is one of the dominant waveforms in wireless communication systems due to its efficient implementation. However, it suffers from a…
Toward a 6G AI-Native Air Interface
Jakob Hoydis, Fayçal Ait Aoudia, Alvaro Valcarce +1
Each generation of cellular communication systems is marked by a defining disruptive technology of its time, such as orthogonal frequency division multiplexing (OFDM) for 4G or Mas…
Machine Learning for MU-MIMO Receive Processing in OFDM Systems
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such met…
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
Fayçal Ait Aoudia, Jakob Hoydis
We introduce a trainable coded modulation scheme that enables joint optimization of the bit-wise mutual information (BMI) through probabilistic shaping, geometric shaping, bit labe…
Deep HyperNetwork-Based MIMO Detection
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Conventional heuristic algorithms are either too complex to be practic…
Trainable Communication Systems: Concepts and Prototype
Sebastian Cammerer, Fayçal Ait Aoudia, Sebastian Dörner +3
We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-…
Towards Hardware Implementation of Neural Network-based Communication Algorithms
Fayçal Ait Aoudia, Jakob Hoydis
There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead…
Model-free Training of End-to-end Communication Systems
Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present i…
Deep Reinforcement Learning Autoencoder with Noisy Feedback
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis
End-to-end learning of communication systems enables joint optimization of transmitter and receiver, implemented as deep neural network-based autoencoders, over any type of channel…
End-to-End Learning of Communications Systems Without a Channel Model
Fayçal Ait Aoudia, Jakob Hoydis
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present…