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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.

 

New journal article on future hybrid vehicular traffic systems accepted for publication

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09.02.2021 – Although the development of fully autonomous vehicles is one of the major research topics in the Intelligent Transportation Systems domain, the upcoming long-term transition period – the hybrid vehicular traffic – is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels and highly varying communication and coordinates capabilities will share the same road networks, resulting in new problems for traffic management systems and communication network infrastructure providers.

 

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.

 

1 Year Campus Network Planner - Unbroken high response and new 26 GHz planning aid

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05.02.2021 – The Communication Networks Institute, along with the Competence Center 5G.NRW is responding to the very large response and the encouragement of now more than 9200 users, consisting of infrastructure manufacturers, network operators and primarily industrial and municipal users, and is now integrating, in addition to the new overview of previous allocation holders, a planning aid for the frequency allocation started at the beginning of the year by the Federal Network Agency for local, broadband frequency usage in the 24.25 - 27.5 GHz frequency range.

 

CNI Publication on efficient UAV-delivery system concept within the COVID-19 pandemic accepted at IEEE SYSCON 2021

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29.01.2021 – Solutions and concepts to alleviate the impact of the COVID-19 pandemic are also included the scope of on-going research at CNI. We are happy to announce that an article proposed by a team of our researchers (Manuel Patchou, Benjamin Sliwa, and Christian Wietfeld) was accepted at the IEEE International Systems Conference (SYSCON) 2021. A pre-print version is now available online on arXiv. The SYSCON 2021 which shall take place in Vancouver this year is currently planned as a virtual event. The contribution CNI submitted is a concept for a drone-based delivery system operating in an environment featuring constraints induced by the current COVID-19 pandemic, such as social distancing and prioritization of medical supplies.

 

Starting the year with 3 new projects

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26.01.2021 – Good news at the beginning of the year. Grant notifications for no less than three new projects reached the institute and announce the start of all projects at the beginning of 2021. With VIZIT, as well as 5Guarantee and Plan & Play, one federally and two state-wide funded projects with focus on connectivity for automotive applications and 5G campus networks strengthen the CNI's research profile. We are looking forward to challenging research questions and a good collaboration with numerous known and new partners.

 

Merry Christmas and a Happy New Year!

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24.12.2020 – At the end of this truly memorable year we would like to thank all our students, partners and friends for their collaboration and support during these challenging times. Fortunately, up to now we have been spared by any long-term health issues caused by the pandemic and we will continue to be cautious in 2021, Yet, we are happy that both teaching as well as research has been continued without major issues due to jointly defined creative and effective solutions. We hope that you will be able to spend - certainly mostly online- some time with your family and friends. For 2021 we wish you all the best and that we will meet again in person and maybe even without wearing a mask! Until then stay healthy!

 

Release of Book on "Machine Learning for Future Wireless Communications"

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30.11.2020 – We are pleased to annouce that the print version of the novel Wiley/IEEE Press book "Machine Learning for Future Wireless Communications" has been released. For "Part I - Spectrum Intelligence and Adaptive Resource Management", we have contributed the chapter "Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing" which reports key results of subprojects A4 and B4 of the collaborative research center SFB 876. In particular, we present novel context-aware and context-predictive methods that utilize machine learning to opportunistically schedule vehicular sensor data transmissions with respect to the anticipated resource efficiency. The proposed approach is not only able to boost the end-to-end data rate of vehicular data transmissions, it simultaneously reduces the power consumption of the mobile devices and contributes to a more efficient usage of the limited network resources.

 

Master student project group RAC3R (Rapid Assistance through Connected Remotely operated Rescue Robotics) successfully completed

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23.11.2020 – The corona crisis did not prevent the successful conclusion of the project group RAC3R (Rapid Assistance through Connected Remotely operated Rescue Robotics) that presented its final results to the members of the CNI in the last 2020 quarter. The project group is part of the master course Electrical Engineering and Information Technology and consists of a group of students collaboratively taking on a scientific task. Within RAC3R, a group of 6 students was originally tasked with the development and optimization of a rescue robotic platform with a Multi-Radio Access Technology feature. The sanitary measures adopted to contain the Corona pandemic posed an additional challenge: full remote work. It was successfully taken on by the students, as they successfully pivoted and setup a virtual robotic simulation environment with real network interface support. A virtual model of the RAC3R robot and a portable implementation of the software stack to the real world were also delivered. Furthermore, the setup was enriched with network controls allowing to either tailor the network quality to match the simulated environment using raytracing or to freely investigate other specific communication scenarios and edge cases.

 

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