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Daniel Külzer, M.Sc. (TUM)
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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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Publications
| Zitatschlüssel | dk2020wcnc |
|---|---|
| Autor | D.F. Külzer and S. Stanczak and M. Botsov |
| Jahr | 2020 |
| ISBN | 978-1-7281-3106-1 |
| ISSN | 1558-2612 |
| DOI | 10.1109/WCNC45663.2020.9120576 |
| Ort | Seoul, Korea (South), Korea (South) |
| Journal | IEEE Wireless Communications and Networking Conference 2020, May 25-28, Virtual Conference |
| Monat | May |
| Herausgeber | IEEE |
| Zusammenfassung | Autonomous driving will rely on a multitude of connected applications with stringent quality of service (QoS) requirements in terms of low latency and high reliability. At the same time, passengers relieved of steering duty have the opportunity to enjoy infotainment services that are often associated with high data rates, e.g. video streaming. The simultaneous usage of such safety-related and infotainment services leads to diverse QoS requirements which are difficult to satisfy in current wireless networks. In an effort to address this issue, we propose a two-layer predictive resource allocation framework that leverages the services' properties and incomplete channel information. First, we optimize packet transmission times by a so-called statistical look-ahead scheduling to enhance the network's QoS and spectral efficiency based upon channel distribution information. Second, packets are forwarded to an online scheduler according to the outcome of this first optimization. Physical resources are assigned considering the services' QoS requirements and current channel state. We present a novel heuristic that performs real-time resource assignment. Simulations show that our approach has a potential for improving transmission reliability and spectral efficiency. |