Inhalt des Dokuments

Es gibt keine deutsche Übersetzung dieser Webseite.

Daniel Külzer, M.Sc. (TUM)

: © private

Daniel Külzer was born in Munich, Germany, in 1994. He received his B.Sc. and M.Sc. in Electrical Engineering and Information Technology in 2016 and 2018, respectively, from the Technical University of Munich, Germany. In 2018, he was also awarded an engineer’s degree (similar to an M.Sc. in Engineering) from Télécom Paris as part of a double degree program. Besides a one-year stay in France, he studied for one semester at the University of Illinois at Urbana-Champaign, United States, in 2015.

Since 2018, he is working at BMW Group in Munich, Germany, developing connectivity solutions for autonomous driving. There he is involved in national and international research projects for vehicle-to-vehicle and vehicle-to-infrastructure communication.

He is currently working towards the Ph.D. degree under the supervision of Prof. Sławomir Stańczak. His research interests include network optimization techniques, particularly predictive resource allocation, and machine learning for Quality of Service prediction and provisioning in the context of vehicular communication.

Projects

AI4Mobile

Find me also on

Publications

AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios
Zitatschlüssel	Kuel2021VTCAI
Autor	D.F. Külzer, M. Kasparick, A. Palaios, R. Sattiraju, O. D. Ramos-Cantor, D. Wieruch, H. Tchouankem, F. Göttsch, P. Geuer, J. Schwardmann, G. Fettweis, H.D. Schotten and S. Stanczak
Jahr	2021
Journal	IEEE Vehicular Technology Conference (VTC Spring) 2021, April 25-28, in Helsinki, Finland
Zusammenfassung	The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.