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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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Predictive Resource Allocation for Automotive Applications using Interference Calculus
Citation key dk2020gc
Author D.F. Külzer and S. Stanczak and R. L.G. Cavalcante and M. Botsov
Year 2020
Journal IEEE Globecom 2020, December 7-11, in Taipei, Taiwan
Editor IEEE
Abstract In autonomous driving, several safety-related connected applications will co-exist with infotainment services for passenger entertainment. Serving the resulting set of diverse quality of service (QoS) requirements poses a tremendous challenge for future cellular networks. For example, safety-related applications require low latency, while infotainment services are associated with high throughput demands. To address the co-existence challenge, we propose a multi-cell anticipatory networking framework with interference coordination based on channel distribution information. The iterative approach first optimizes packet transmission times by so-called statistical look-ahead scheduling leveraging service properties. Interference calculus is applied for estimating the network's load in each step. Finally, packets are forwarded to an online scheduler based on the found transmission schedule. Simulations show that inter-cell interference management is crucial in provisioning the desired QoS. The iterative optimization framework offers superior transmission reliability and spectral efficiency.
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Daniel Külzer, M.Sc.
BMW Group
Petuelring 130
80788 München