ET6: ML AeroCO

Machine Learning for Communications in Aviation

This I³project targets the application of Machine Learning (ML) in aeronautical communications. In contrary to many engineering applications, which view the learning process as a black box, this project puts a particular focus on the understanding and interpretation of the learning process. In short, the target can be described as a fundamental understanding of what ML can and does do in the context of communication networks.

By combining the expertise from our institute with that of the Institute of Communications, ML is considered on the data link layer. In the presence of coexisting legacy systems, opportunistic spectrum access is studied, which is achieved through the learning of the legacy system's behavior, so that future behavior becomes predictable, and the new system can opportunistically access the spectrum when the legacy system does not.

Funding

This project is funded by the Hamburg University of Technology as an I3 project.

Project Partners

This project is conducted in cooperation with the Institute of Communications of Prof. Bauch at the TUHH.

Project Members

Publications

2023

Hidden node-aware dynamic spectrum access using deep learning for coexisting aeronautical communication systems
Schulz, Leonard Paul; Kopyto, David Jonas; Stolpmann, Daniel; Lindner, Sebastian; Bauch, Gerhard; Timm-Giel, Andreas
IEEE 97th Vehicular Technology Conference (VTC 2023 Fall)
Publisher DOI

2022

Deep Learning-Based Dynamic Spectrum Access for Coexistence of Aeronautical Communication Systems
Kopyto, David Jonas; Lindner, Sebastian; Schulz, Leonard Paul; Stolpmann, Daniel; Bauch, Gerhard; Timm-Giel, Andreas
96th IEEE Vehicular Technology Conference (VTC Fall 2022)
Publisher DOI

2021

Time- and frequency-domain dynamic spectrum access: learning cyclic medium access patterns in partially observable environments
Lindner, Sebastian; Stolpmann, Daniel; Timm-Giel, Andreas
Electronic Communications of the EASST 80: 1-14 (2021)
Open Access | Publisher DOI

2020

Predictive scheduling and opportunistic medium access for shared-spectrum radio systems in aeronautical communication
Fisser, Leonard; Lindner, Sebastian; Timm-Giel, Andreas
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.: Deutscher Luft- und Raumfahrtkongress 2020. - Dokument 530331 (2020)
Open Access | Publisher DOI
Coexistence of Shared-Spectrum Radio Systems through Medium Access Pattern Learning using Artificial Neural Networks
Lindner, Sebastian; Fisser, Leonard; Timm-Giel, Andreas
32nd International Teletraffic Congress (ITC 2020)
Publisher DOI

TU Hamburg

Institute of Communication Networks (E-4)

Prof. Dr.-Ing. Andreas Timm-Giel

Am Schwarzenberg-Campus 3, Room E1.058

21073 Hamburg

E-mail : comnets@tuhh.de

Phone : +49 40 42878-3049/3249