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Institut für TelekommunikationssystemeDaniel Külzer, M.Sc.


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Daniel Külzer, M.Sc. (TUM)


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.



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A landing spot approach for enhancing the performance of UAV-aided wireless networks
Zitatschlüssel dk2018Icc
Autor R. Gangula, D. Gesbert, D.F. Külzer and J.M. Franceschi
Jahr 2018
ISBN 978-1-5386-4328-0
ISSN 2474-9133
DOI 10.1109/ICCW.2018.8403622
Ort Kansas City, MO, USA
Journal IEEE International Conference on Communications Workshops (ICC Workshops), May 20-24, 2018, in Kansas City, United States
Monat May
Herausgeber IEEE
Zusammenfassung UAV-aided wireless networks allow ultra-flexible deployment of wireless resources when and where it matters. Despite their promise, such networks are severely hindered by the limited on-board battery budget. This paper introduces a novel yet simple approach to circumvent this problem, based on the concept of so-called landing spots (LSs). LSs allow to trade-off throughput for rest time. We also derive a dynamic program which optimally exploits any given LS setup for UAV trajectory design. Our study shows that LSs dramatically enhance the lifetime of flying radio access networks while only moderately affecting the throughput performance. In IoT data- harvesting settings, LSs substantially increase the total collected data payload for a given battery budget.
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