Sprungmarken

Servicenavigation

Hauptnavigation

Sie sind hier:

Hauptinhalt

Lehrstuhl für Kommunikationsnetze

 

CNI Banner

Willkommen auf den Internetseiten des Lehrstuhls für Kommunikationsnetze

Aktuelle Meldungen

CNI at High-profile Event on NRW's Digitalization Strategy

5GmmWave

02.11.2018 – CNI has been invited to present its latest 5G research results at the Digitalkonferenz.NRW. Following the invitation of Prime Minister of North Rhine-Westphalia Armin Laschet and Minister of Economics and Digital Affairs Prof. Dr. Andreas Pinkwart, over 500 guests from politics, business, science, culture and society came together to discuss the digital strategy for North Rhine-Westphalia with the state government on October 26, 2018.

At our booth we presented a 5G mmWave antenna tracking system, implementing a mmWave link at 28 GHz between a fixed base station equipped with a highly directional and electrically steerable pencil beam antenna and an Unmanned Aerial Vehicles (UAV). Beamforming and pencil beam antennas are expected to become a major component of 5G mmWave New Radio networks. While spatial separation and high gains are anticipated benefits, the suitability of those new antenna types in highly dynamic scenarios, such as the use on UAVs, requires appropriate real–time steering capabilities and needs to be proven in practice. In our testbed, a rail system is customized to generate precisely reproducible mobility trajectories of the UAV. In case the antennas are aligned within a given error margin, a stable air–to–ground highspeed connection (up to 3 Gbps) can be observed during the flight experiments.

 

TU Dortmund's CNI contributes to the establishment of the German Centre for Rescue Robotics in Dortmund

DRZ-CNI

28.10.2018 – On October 1, 2018, a new project has started - funded by the German Ministry for Education and Research (BMBF) - which aims to establish a national competence center for rescue robotics. For the first time in Germany will first responders, researchers and industry work closely together to realize autonomous rescue robots, which will become part of national as well as international robotic task forces for emergency rescue operations. Additionally, new test procedures will be developed, which will form the basis for standardization and certification of different robot systems acting outdoor on the ground, in the air, in water, as well as indoor. Based on its long-term track record in this research area, the Communication Networks Institute (CNI) will lead the work related to robust wireless communications of robotic systems, which allow for reliable operation even in challenging conditions of rescue operations

 

Excellent Paper Award at International Conference for 5G Edge Clouds

ICTC2018_Kurtz

18.10.2018 – We are delighted to announce that the contribution “Evaluating Software-Defined Networking-Driven Edge Clouds for 5G Critical Communications”, by Fabian Kurtz, Igor Laukhin, Caner Bektas and Christian Wietfeld, was awarded as Excellent Paper at the 9th International Conference on ICT Convergence (ICTC) 2018. The flagship conference of the Korean Institute of Communications and Information Sciences, co-sponsored by the IEEE, took place in October in Jeju, South Korea. Within the context of the BMBF funded Franco-German project BERCOM (Blueprint for Pan-European Resilient Critical Infrastructures based on LTE Communications), the research discussed in the paper demonstrated the advantages of Edge Clouds in the scope of 5G critical infrastructure communications.

 

Dissertation on Software-Defined Networking for Energy Systems successfully completed

Dorsch Promotion 2018 klein

08.10.2018 – The CNI team is happy to announce that Nils Dorsch has successfully defended his dissertation on October 2, 2018. The dissertation titled "Software-Defined Networking (SDN) for Real-Time Capable, Reliable and Cost-Efficient Smart Grid Communication Infrastructures" has proposed and investigated innovative concepts for communication networks suitable for the so-called Smart Grid. The work has been carried out within the interdisciplinary DFG-funded research group 1511 on "Control and protection systems for the future energy system". Dr. Dorsch has proposed a dedicated SDN interface which enables energy system operators to control the underlying communication system through a so-called north-bound interface. These extension of the Floodlight SDN controller originally developed at Stanford University have been also made available as open source. As a unique solution approach, Nils Dorsch has integrated the Network Calculus method into the real-time SDN controller to ensure that the maximum delay bounds required by the energy system are met. The work of Nils Dorsch has attracted considerable attention: a first paper describing the overall system concept has been cited over 50 times since its publication in 2014 at the IEEE SmartGridComm conference. An analysis of the economic impact of SDN-based networks has been recently published as journal article in NETNOMICS (Springer). Another journal article on "Enabling Hard Service Guarantees in Software-Defined Smart Grid Infrastructures" is accepted to appear in Computer Networks (Elsevier).

 

IEEE Student Fellowship for Outstanding Contributions to Machine Learning in Communications based on research in DFG SFB 876

Sliwa Scholarship 2018 Web

11.09.2018 – We are very happy to announce that Benjamin Sliwa has received the 2018 Transportation Electronics Fellowship Award "For Outstanding Student Research Contributions to Machine Learning in Vehicular Communications and Intelligent Transportation Systems" of the Vehicular Technology Society (VTS) within the IEEE, which grants two student fellowships per year worldwide. The bestowal took place at the IEEE Vehicular Technology Conference (VTC) Fall 2018, in August in Chicago, USA, which is the flagship conference of the VTS. Within his research in project B4 "Analysis and Communication for Dynamic Traffic Prognosis" of the Collaborative Research Centre (SFB 876), Benjamin Sliwa has presented pioneering work in using machine learning for resource-efficient data transmissions within cellular vehicular networks.

 

Nebeninhalt