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2021

International panel on 6G organized by CNI as part of 5G.NRWeek 2021

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08.09.2021 – As CNI's main contribution to this year's 5G.NRWeek, an international panel on 6G took place on 8 September 2021 as online webinar. Distinguished panel speakers from Finland, Belgium and Germany provided their long-term vision on the future development of 6G, which is expected to be launched in 2030. The slides and a recoding of the complete session will become online for those who missed the session. Already on Monday, 6 Sept 2021, a hybrid conference took place in Dusseldorf took place with Prof. Andreas Pinkwart, NRW's minister for economy, innovation, digitalization and energy, as key note speaker. Prof. Christian Wietfeld, member of the advisory board of the competence centre on 5G in NRW, participated as panelist in the session on network slicing. The recorded stream of the complete event is also available online. Finally, CNI co-organized a virtual exhibition displaying 25 5G deployment and research examples throughout NRW. The event clearly demonstrated the strength of NRW's activities on 5G and 6G networks.

 

CNI contributes to 6GEM research hub

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29.06.2021 – With their proposal "6GEM open - efficient - secure - safe", RWTH Aachen University, Ruhr University Bochum, TU Dortmund University and the University of Duisburg-Essen were successful in a call for proposals issued by the German Federal Ministry of Education and Research (BMBF). Based on CNI's contributions to the competence center CC5G.NRW and participation in numerous 5G projects (DFG SFB 876, 5GAIN, 5Guarantee and Plan&Play), TU Dortmund University will research novel, real-time capable 6G network technologies and innovative 6G application fields, especially in close cooperation with Fraunhofer IML and the German Rescue Robotics Center (DRZ). The results will contribute to the standardization of open 6G networks (Open RAN Alliance), open source projects for software-defined networks and patents. Prof. Dr.-Ing. Christian Wietfeld will be the 6GEM spokesperson for the TU Dortmund University.

 

Best Student Paper Award at IEEE VTS Flagship Conference IEEE VTC-Spring 2021

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10.05.2021 – We are very happy to announce that the CNI Researchers Benjamin Sliwa, Cedrik Schüler, Manuel Patchou, and Christian Wietfeld have received the prestigious "Best Student Paper Award" for the paper "PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge" at the IEEE Vehicular Technology Conference (VTC-Spring) 2021 which is the flagship conference of the Vehicle Technology Society (VTS) of the IEEE.

 

Publication on „A Low Cost Modular Radio Tomography System for Bicycle and Vehicle Detection and Classification“ to be presented on IEEE SYSCON 2021 conference

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08.03.2021 – Another paper regarding CNI's research activities on a low-cost and modular radio-based detection and classification system for road users has been accepted for IEEE International Systems Conference (SYSCON) 2021. Compared to existing solutions, the proposed approach meets multiple crucial requirements, including cost-efficiency, robustness, accuracy, and privacy-preservation, while classifying heterogeneous road users (e.g., cyclists, passenger cars) accurately.

 

Publication on „SAMUS: Slice-Aware Machine Learning-based Ultra Reliable Scheduling“ to be presented on IEEE flagship conference ICC 2021

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08.02.2021 – 5G network slicing is particularly important for critical infrastructure communications researched at the CNI, especially in the context of so-called "mixed-critical" services, as multiple service types such as Ultra-Reliable Low Latency Communication (uRLLC) and Enhanced Mobile Broadband (eMBB) are envisioned to be incorporated into a single physical communication network. To balance the needs of low latency slices and demanding high bitrate best effort slices, the data-driven scheduler SAMUS was developed and evaluated at the CNI, which effectively minimizes latency for critical infrastructure slices while providing the maximum data rate possible for other participants in the network based on Machine Learning.