963 citations · 3.4k across the 52 of their papers we have counts for
71 papers
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…
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer +4
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulatio…
DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems
Reinhard Wiesmayr, Chris Dick, Jakob Hoydis +1
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…
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Pengzhi Huang, Emre Gönültaş, Maximilian Arnold +3
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise posit…
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…
Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming
Kai Kang, Qiyu Hu, Yunlong Cai +3
In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedica…
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…
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications
K. Pavan Srinath, Jakob Hoydis
This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receiver…
Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory
K. Pavan Srinath, Jakob Hoydis
Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel…
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier
Dani Korpi, Mikko Honkala, Janne M. J. Huttunen +2
Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jo…
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
Qiyu Hu, Yunlong Cai, Kai Kang +3
In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, w…
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Brian Rappaport, Emre Gönültaş, Jakob Hoydis +3
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state informa…
Learning OFDM Waveforms with PAPR and ACLR Constraints
Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis +1
An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characterist…
Waveform Learning for Next-Generation Wireless Communication Systems
Fayçal Ait Aoudia, Jakob Hoydis
We propose a learning-based method for the joint design of a transmit and receive filter, the constellation geometry and associated bit labeling, as well as a neural network (NN)-b…
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce +1
In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) t…
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…
Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis
Faris B. Mismar, Jakob Hoydis
This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications…
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…
Bayesian Optimization for Radio Resource Management: Open Loop Power Control
Lorenzo Maggi, Alvaro Valcarce Rial, Jakob Hoydis
We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resour…