Collaborative Research Center 876
The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
Project duration: 01.01.2015 - 31.12.2022
Project Composition of the SFB 876
The collaborative research center is divided into multiple projects. An extensive overview of all projects and their main objective can be found on the homepage of the SFB 876.
The Communication Networks Institute attends projects A4 and B4
News
CNI researchers receive Best Paper Award for new Open Source Network Simulator for Narrowband-Internet-of-Things (NB-IoT) Communications
We are pleased to announce our contribution “From LENA to LENA-NB: Implementation and Performance Evaluation of NB-IoT and Early Data Transmission in…
CNI congratulates Melina Geis, Tim Gebauer, and Cedrik Schüler for receiving prestigious awards for their master’s theses
We are very happy to announce that three of our previous students, who have all become researchers at the Communication Networks Institute, have…
Best Paper of the Best Paper Session @ 11th International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2021
We are delighted to announce that the contribution “Accurate Multi-Zone UWB TDOA Localization utilizing Cascaded Wireless Clock Synchronization” by…
Release of Book on "Machine Learning for Future Wireless Communications"
We are pleased to annouce that the print version of the novel Wiley/IEEE Press book "Machine Learning for Future Wireless Communications" has been…
Best Paper Award at prestigious IEEE Conference
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…
CNI contribution to the 2nd 6G Wireless Summit
We are very pleased to announce that our paper "Towards cooperative data rate prediction for future mobile and vehicular 6G networks" has been…
Publications
3D Self-Motion Tracking Services: Coalescence of mmWave Beam Orientations and Phase Information
S. Häger, S. Böcker, C. Wietfeld
In 2022 IEEE Future Networks World Forum (FNWF), Montreal, Canada, October 2022.
TinyDRaGon: Lightweight Radio Channel Estimation for 6G Pervasive Intelligence
M. Geis, B. Sliwa, C. Bektas, C. Wietfeld
In 2022 IEEE Future Networks World Forum (FNWF), October 2022.
From LENA to LENA-NB: Implementation and Performance Evaluation of NB-IoT and Early Data Transmission in ns-3
P. Jörke, T. Gebauer, C. Wietfeld
In Workshop on ns-3 (WNS3), Online, Juni 2022.
Scaling Dense NB-IoT Networks to the Max: Performance Benefits of Early Data Transmission
P. Jörke and T. Gebauer and S. Böcker, C. Wietfeld
In 2022 IEEE 95th Vehicular Technology Conference: VTC2022-Spring.
System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving
H. Schippers, C. Schüler, B. Sliwa, C. Wietfeld
In 2022 Annual IEEE International Systems Conference (SysCon), Virtual Event.
QoE Evaluation of Real-Time Remote Operation with Network Constraints in a System-of-Systems
C. Schüler, T. Gebauer, M. Patchou, C. Wietfeld
In 2022 Annual IEEE International Systems Conference (SysCon), Virtual Event.
DRaGon: Mining latent radio channel information from geographical data leveraging deep learning
B. Sliwa, M. Geis, C. Bektas, M. Lopéz, P. Mogensen, C. Wietfeld
In 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas
Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference
J. Tiemann, J. Friedrich, C. Wietfeld
In Sensors, vol. 22, no. 4, 2022.
Machine learning for resource-efficient data transfer in mobile crowdsensing
B. Sliwa, R. Falkenberg, C. Wietfeld
Chapter in Machine Learning for Future Wireless Communications (F. L. Luo, ed.), John Wiley & Sons Inc, February…
Search & People Search
Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B 1 / A 40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from North Campus to South Campus by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at North Campus and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 20 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.
The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent “Technologiepark”.
Site Map of TU Dortmund University (Second Page in English).