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CNI joins firefighting exercise in rescue robotics project

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29.08.2020 – As part of the establishment of German Rescue Robotics Center (DRZ), CNI is working on reliable communications solutions for use in disaster scenarios. An exercise for firefighters near the German-Dutch border (Viersen district) in early September acted as a backdrop for field-testing interoperability and performance of systems developed in the research project.

 

CNI expands 5G research by successfully acquiring further research projects

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02.07.2020 – We are pleased to announce that the Communication Networks Institute successfully acquired two projects funded by the 5G.NRW program. This reaffirms the institute's accomplishments, enabling us to sustainably expand and strengthen the focus on 5G research. With the role as consortium leader in the Plan & Play project and as a partner in the 5Guarantee project, we will contribute significantly to accelerating 5G as a key technology for demanding applications in industrial campus networks.

 

Best Paper Award at prestigious IEEE Conference

IEEE ICC Best Paper Award Certificate

01.07.2020 – We are very happy to announce that a joint work developed within the DFG SFB 876 between machine-learning (Nico Piatkowski from the Computer Science department) and communication experts (Benjamin Sliwa, Christian Wietfeld from CNI) has received a best paper award at the IEEE flagship conference International Communications Conference (ICC). At ICC 2020 which was originally planned to take place in Dublin this year over 2100 papers were presented in a virtualized format. The award-winning contribution of TU Dortmund "LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations" presents the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. In two case studies focusing on cellular data rate prediction and radio-based vehicle classification, LIMITS is validated by comparing different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB. Furthermore its potential to catalyze the development of machine learning enabled IoT systems is demonstrated.

 

CNI contribution to MDPI Drones Journal on Multi-Link Communication Experiments over the German Baltic Sea

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06.05.2020 – We are very pleased to announce that our paper "Reliable Long Range Multi-Link Communication for Unmanned Search and Rescue Aircraft Systems in Beyond Visual Line of Sight Operation" has been published in the MDPI Drones Open Access Journal.

 

CNI successfully contributes to 6G White Paper Expert Groups

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30.04.2020 – While 5G is a major focus of the on-going research at CNI, we are also looking further ahead: CNI researchers have participated in recent months in two selected international expert groups which aimed to formulate 6G Research Visions. The white paper drafts are now publicly available online on arXiv. CNI representatives have specifically contributed to the White papers on "Machine Learning in Wireless Communications" and "Critical and Massive Machine-Type-Communication towards 6G". It was orignally planned that the 12 white paper working groups would meet in March 2020 at the 2nd 6G Summit in Finland. Due to the on-going travel and contact restrictions, the summit was organized as virtual event. The working groups continued their work successfully remotely from the respective home offices of the contributors.

 

100 Days Campus Network Planning Tool - A Statistical Analysis

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28.04.2020 – Free offer of the Competence Center 5G.NRW is very well received.

At the beginning of the year the Competence Center 5G.NRW published the free service of the 5G Campus Network Planner (in German: 5G Campusnetzplaner). The aim of this service is to provide companies with a quick introduction to the local 5G radio license and to determine the frequency fee that is likely to be charged for the company-specific campus network in a few intuitive steps. In this article, an analysis of the service's usage statistics is conducted.

 

We are ready – Introductory presentations to the online CNI courses of the summer semester are available as video streams

Photograph of Professor Wietfeld in the Newly Established Online Teaching Workspace at CNI

15.04.2020 – In preparation for the online teaching in the summer semester 2020 that will start next week, Prof. Wietfeld explains in two introductory videos the concept of the chair for the two key courses of the summer semester "Business Administration Basics of ICT" (Bachelor) and "Mobile Networks" (Master). Lectures and exercises are carried out as live streams with interaction possibilities for the students (chat, request to speak via video). The streams are recorded and subsequently made available in the student portal moodle, as well as the slide material as PDF. The components of the practical training, i.e. the business simulation as well as the development of a business plan for a self-chosen ICT innovation, will be possible from the home office with appropriate online collaboration tools. All students interested in the courses are asked to register via moodle to receive invitations to the respective online meetings. We are looking forward to an intensive and fruitful collaboration, despite the new boundary conditions.

 

On-going remote research and preparations for online teaching at CNI

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22.03.2020 – In spite of the special "stay-at-home" situation imposed by the measures against the spreading of the Corona virus, the CNI team is committed to continue research and teaching by leveraging communication networks and corresponding online collaboration tools as much as possible. To all our students and partners: Stay healthy!

Please see the homepage of the TU Dortmund University and the faculty for general announcements and advice on the Corona virus situation.

 

CNI congratulates Stefan Falten for receiving the Hans-Uhde-Award 2020

Award-Winning Graduates with Professor Ursula Gather and Guido Baranowski

19.03.2020 – We are very happy to announce that Stefan Falten has been awarded with a Hans-Uhde-Prize for his outstanding master’s thesis “Implementation and experimental validation of a simulation model for the B.A.T.M.A.N. V routing protocol“. Within his thesis at the Communication Networks Institute, Mr. Falten has analyzed and optimized the performance of the novel bio-inspired routing protocol “Better Approach to Mobile Ad-hoc Networking V” (B.A.T.M.A.N. V) within highly dynamic ground-based and aerial vehicular networks. In order to assess the behavior of the routing protocol under challenging mobility conditions, he has transferred the existing Linux kernel module of B.A.T.M.A.N. V into an OMNeT++ simulation model which has been validated in real world experiments. The results of the thesis have made a significant contribution to a scientific publication, which has been presented at the international conference IEEE Vehicular Technology Conference (VTC-Spring) 2019.

 

CNI contribution to the 2nd 6G Wireless Summit

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28.01.2020 – We are very pleased to announce that our paper "Towards cooperative data rate prediction for future mobile and vehicular 6G networks" has been accepted for being presented at the 2nd 6G Wireless Summit. In the paper, we bring together key results of two subprojects of the Collaborative Research Center "SFB 876" and take a look beyond the ongoing 5G standardization efforts that solely focus on bringing machine learning-enabled intelligence to the network side. Using the SDR-based passive control channel sniffer FALCON, we aim to mimic the behavior of a possible future 6G deployment where the network infrastructure actively provides information about the current spectral resource utilization to the mobile clients in order to improve client-based data rate prediction for anticipatory communications. In a real world proof-of-concept study in a vehicular scenario, we were able to improve reduce the average prediction error by up to 30% through the proposed cooperative approach.

 

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