b'Jakob Hoydis'

research

∙ 03/20/2023

Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling

Sionna is a GPU-accelerated open-source library for link-level simulatio...

0 Jakob Hoydis, et al. ∙

research

∙ 12/15/2022

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

Iterative detection and decoding (IDD) is known to achieve near-capacity...

0 Reinhard Wiesmayr, et al. ∙

research

∙ 11/15/2022

Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems

Localization services for wireless devices play an increasingly importan...

0 Pengzhi Huang, et al. ∙

research

∙ 07/29/2022

Graph Neural Networks for Channel Decoding

In this work, we propose a fully differentiable graph neural network (GN...

0 Sebastian Cammerer, et al. ∙

research

∙ 06/08/2022

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-...

0 Kai Kang, et al. ∙

research

∙ 05/22/2022

Deep Learning-Based Synchronization for Uplink NB-IoT

We propose a neural network (NN)-based algorithm for device detection an...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 03/22/2022

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

Sionna is a GPU-accelerated open-source library for link-level simulatio...

0 Jakob Hoydis, et al. ∙

research

∙ 03/11/2022

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications

This is the second part of a two-part paper that focuses on link-adaptat...

0 K. Pavan Srinath, et al. ∙

research

∙ 03/11/2022

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

Link-adaptation (LA) is one of the most important aspects of wireless co...

0 K. Pavan Srinath, et al. ∙

research

∙ 01/14/2022

Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier

Machine learning (ML) has shown great promise in optimizing various aspe...

0 Dani Korpi, et al. ∙

research

∙ 10/22/2021

Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding

In this paper, we propose an end-to-end deep learning-based joint transc...

0 Qiyu Hu, et al. ∙

research

∙ 10/21/2021

Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer

Channel charting is an emerging technology that enables self-supervised ...

0 Brian Rappaport, et al. ∙

research

∙ 10/21/2021

Learning OFDM Waveforms with PAPR and ACLR Constraints

An attractive research direction for future communication systems is the...

0 Mathieu Goutay, et al. ∙

research

∙ 09/02/2021

Waveform Learning for Next-Generation Wireless Communication Systems

We propose a learning-based method for the joint design of a transmit an...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 08/16/2021

The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning

In this paper, we propose a new framework, exploiting the multi-agent de...

0 Mateus P. Mota, et al. ∙

research

∙ 06/30/2021

Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems

Machine learning (ML) can be used in various ways to improve multi-user ...

0 Mathieu Goutay, et al. ∙

research

∙ 06/30/2021

End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints

Orthogonal frequency-division multiplexing (OFDM) is widely used in mode...

0 Mathieu Goutay, et al. ∙

research

∙ 06/29/2021

End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping

As communication systems are foreseen to enable new services such as joi...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 04/14/2021

Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis

This letter demonstrates the use of unsupervised machine learning to ena...

0 Faris B. Mismar, et al. ∙

research

∙ 01/20/2021

Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning

Orthogonal frequency division multiplexing (OFDM) is one of the dominant...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 12/15/2020

Bayesian Optimization for Radio Resource Management: Open Loop Power Control

The purpose of this paper is to provide the reader with an accessible ye...

0 Lorenzo Maggi, et al. ∙

research

∙ 12/15/2020

Towards a 6G AI-Native Air Interface

Each generation of cellular communication systems is marked by a definin...

0 Jakob Hoydis, et al. ∙

research

∙ 12/15/2020

Machine Learning for MU-MIMO Receive Processing in OFDM Systems

Machine learning (ML) starts to be widely used to enhance the performanc...

7 Mathieu Goutay, et al. ∙

research

∙ 09/11/2020

End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication

Previous studies have demonstrated that end-to-end learning enables sign...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 07/20/2020

Towards Joint Learning of Optimal Signaling and Wireless Channel Access

Communication protocols are the languages used by network nodes to accom...

0 Alvaro Valcarce, et al. ∙

research

∙ 04/10/2020

Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems

We introduce a trainable coded modulation scheme that enables joint opti...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 02/07/2020

Deep HyperNetwork-Based MIMO Detection

Optimal symbol detection for multiple-input multiple-output (MIMO) syste...

0 Mathieu Goutay, et al. ∙

research

∙ 11/29/2019

Trainable Communication Systems: Concepts and Prototype

We consider a trainable point-to-point communication system, where both ...

0 Sebastian Cammerer, et al. ∙

research

∙ 09/14/2019

Learning to Communicate and Energize: Modulation, Coding and Multiple Access Designs for Wireless Information-Power Transmission

The explosion of the number of low-power devices in the next decades cal...

0 Morteza Varasteh, et al. ∙

research

∙ 08/30/2019

Learning Modulation Design for SWIPT with Nonlinear Energy Harvester: Large and Small Signal Power Regimes

Nonlinear energy harvesters (EH) behave differently depending on the ran...

0 Morteza Varasteh, et al. ∙

research

∙ 07/02/2019

"Machine LLRning": Learning to Softly Demodulate

Soft demodulation, or demapping, of received symbols back into their con...

0 Ori Shental, et al. ∙

research

∙ 06/18/2019

Joint Learning of Geometric and Probabilistic Constellation Shaping

The choice of constellations largely affects the performance of communic...

0 Maximilian Stark, et al. ∙

research

∙ 06/11/2019

Adaptive Neural Signal Detection for Massive MIMO

Symbol detection for Massive Multiple-Input Multiple-Output (MIMO) is a ...

0 Mehrdad Khani, et al. ∙

research

∙ 05/20/2019

Transmitter Classification With Supervised Deep Learning

Hardware imperfections in RF transmitters introduce features that can be...

0 Cyrille Morin, et al. ∙

research

∙ 04/06/2019

Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed ...

0 Luca Sanguinetti, et al. ∙

research

∙ 03/21/2019

Smart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come

Future wireless networks are expected to constitute a distributed intell...

0 Marco Di Renzo, et al. ∙

research

∙ 02/19/2019

Towards Hardware Implementation of Neural Network-based Communication Algorithms

There is a recent interest in neural network (NN)-based communication al...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 02/11/2019

Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "...

0 Emil Björnson, et al. ∙

research

∙ 01/08/2019

Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction

A major obstacle for widespread deployment of frequency division duplex ...

0 Maximilian Arnold, et al. ∙

research

∙ 12/14/2018

Model-free Training of End-to-end Communication Systems

The idea of end-to-end learning of communication systems through neural ...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 11/08/2018

Fundamental Asymptotic Behavior of (Two-User) Distributed Massive MIMO

This paper considers the uplink of a distributed Massive MIMO network wh...

0 Luca Sanguinetti, et al. ∙

research

∙ 11/05/2018

Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO Spectral Efficiency

This paper analyzes how the distortion created by hardware impairments i...

0 Emil Björnson, et al. ∙

research

∙ 10/12/2018

Deep Reinforcement Learning Autoencoder with Noisy Feedback

End-to-end learning of communication systems enables joint optimization ...

0 Mathieu Goutay, et al. ∙

research

∙ 07/02/2018

Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes

We demonstrate that error correcting codes (ECCs) can be used to constru...

0 Stefan Schibisch, et al. ∙

research

∙ 05/21/2018

Can Hardware Distortion Correlation be Neglected When Analyzing Uplink SE in Massive MIMO?

This paper analyzes how the distortion created by hardware impairments i...

0 Emil Björnson, et al. ∙

research

∙ 04/06/2018

End-to-End Learning of Communications Systems Without a Channel Model

The idea of end-to-end learning of communications systems through neural...

0 Fayçal Ait Aoudia, et al. ∙

research

∙ 03/15/2018

OFDM-Autoencoder for End-to-End Learning of Communications Systems

We extend the idea of end-to-end learning of communications systems thro...

0 Alexander Felix, et al. ∙

research

∙ 07/11/2017

Deep Learning-Based Communication Over the Air

End-to-end learning of communications systems is a fascinating novel con...

0 Sebastian Dörner, et al. ∙

Jakob Hoydis

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