389.174 Seminar Wireless Communications

Seminar Wireless Communications


Course No. 389.174 (TISS Link)
2019S, SE, 3.0h, 3.0EC

This year’s topic of the mobile communications seminar is focusing on mobility and hybrid cloud access as the enabler for wireless networks of the future. 5G networks are expected to be significantly different from today’s networks. The main requirements are: support up to 1000 times the capacity, reduce the latency of data delivery, flatten total energy consumption and finally make the network to be self-aware. These tasks will need new concepts in which capacity will follow users movements and flows rather than coverage. Other new challenges are the support of remote radio locations, radio over fibre, relays in moving mass transportation and finding new data sources to track user flows in different direction. In this lecture, we will hear talks and work on paper on the how to reach these goals.


General Information

In the first part of the seminar, university researchers present their latest research in their field in 5G and new applications as well as challenges for 5G.

In the second part of the seminar, students will read literature and research papers on antenna systems for 5G and reflect their results by their own presentations. Please choose one paper from our suggested paper list and report to Philipp Svoboda till 30.03. Papers will be assigned on a first-come-first-serve basis. Note: you can also bring your own topic/paper, the list is only a suggestion.

The round of student talks will start after the Easter holidays on Thursdays with 2-3 Students per session. A list of dates will be put online after paper registration.

Language: English

 Mode

The attendance of the seminar is compulsory! We will keep records of your attendance. The seminar starts with invited talks, after that the students give self-prepared presentations (~30min). Each student has to prepare a written report that is due at the end of the semester (at the latest June 15!) (~15pages).

The talks will take place on in June, more details will be announced in the course.

Beachten Sie beim Verfassen der Ausarbeitung bitte die Richtlinie der TU Wien zum Umgang mit Plagiaten: https://www.tuwien.ac.at/fileadmin/t/ukanzlei/Lehre_-_Leitfaden_zum_Umgang_mit_Plagiaten.pdf

Please consider the plagiarism guidelines of TU Wien when writing your seminar paper: http://www.tuwien.ac.at/fileadmin/t/ukanzlei/t-ukanzlei-english/Plagiarism.pdf

 

Hours per week: 3.0

 

Involved persons:

Univ.Prof. Peter Farkas,
Dr. Martin Slanina,
Univ.Prof. Markus Rupp,
+ invited researchers.

 


Dates:

Vorbesprechung: Donnerstag. 4.3.2020, 12:30 Uhr, in Zoom

Zoom Link: https://tuwien.zoom.us/j/97241397442?pwd=RDdRL1pLY2tLb1dTem55dEQzRm8xQT09

Meetings
4.3. Vienna – Meeting point Zoom (https://tuwien.zoom.us/j/97241397442?pwd=RDdRL1pLY2tLb1dTem55dEQzRm8xQT09) @ 13.00
  • xxx
11.3. Bratislava – Meeting Point Zomm (see above) 13.00 (2h)
  • Virtualized RAN as a service; Ing. Martin Macuha, PhD. Technology Consulting Manager at Accenture
18.3. Bratislava – Meeting Point Zomm (see above) 13.00 (2h)
  • 5G –  initial year in review & what to expect next Ing. Matú? Turcsány, PhD. , Chief Technology Officer Responsible for Czech Republic, Hungary, Slovakia and Slovenia 2.
25.3. Vienna – Meeting point Zoom (see above)  @ 13.00 (3h)
  • 13.00 Elmar Trojer (Ericsson Research)
  • 14.00 David Gesbert (EURECOM)

 

8.4. Brno – Meeting point Zoom (see above)  @ 9.30 — 11.30 (2h)
  • ing. Michal Pokorný, Ph.D. (Resideo)
15.4. Brno – Meeting point Zoom (see above)  @ 9.00 — 11.00 (+discussion) (2h)
  • ing. Václav Valenta, Ph.D. (ESA) “RF engineering for advanced satellite telecommunication payloads.”

Bring your documents.


 You might find this helpful: https://www.gcu.ac.uk/library/pilot/researchskills/criticalreviewing/
List of Papers (work in progress):
  1. Chen, Y., Lin, X., Khan, T., & Mozaffari, M. (2019). Efficient drone mobility support using reinforcement learning. ArXiv, 2–7.
  2. Hu, Y., Chen, M., Saad, W., Poor, H. V., & Cui, S. (2020). Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks, 1–31. Retrieved from http://arxiv.org/abs/2012.03158
  3. Dai, A., Li, R., Zhao, Z., & Zhang, H. (2020). Graph Convolutional Multi-Agent Reinforcement Learning for UAV Coverage Control, 1106–1111. https://doi.org/10.1109/wcsp49889.2020.9299760
  4. Cao, Y., Lien, S. Y., & Liang, Y. C. (2020). Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks. IEEE Transactions on Communications, 6778(c), 1–15. https://doi.org/10.1109/TCOMM.2020.3041347
  5. Liu, C. H., Ma, X., Gao, X., & Tang, J. (2020). Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning. IEEE Transactions on Mobile Computing, 19(6), 1274–1285. https://doi.org/10.1109/TMC.2019.2908171
  6. Gama, F., Isufi, E., Leus, G., & Ribeiro, A. (2020). Graphs, Convolutions, and Neural Networks, (November).
  7. Wu, M., & Li, C. (2020). 5G Wireless Intelligent Propagation Channel Modelling Based on Deep Residual Network. Proceedings – 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2020, 64–70. https://doi.org/10.1109/CTISC49998.2020.00017
  8. Li, T., Zhu, X., & Liu, X. (2020). An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network. IEEE Access, 8, 122229–122240. https://doi.org/10.1109/ACCESS.2020.3006502
  9. Lv, Z., Qiao, L., & You, I. (2020). 6G-Enabled Network in Box for Internet of Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems, 1–8. https://doi.org/10.1109/tits.2020.3034817
  10. Shafin, R., Liu, L., Chandrasekhar, V., Chen, H., Reed, J., & Zhang, J. C. (2019). Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G. ArXiv, (April), 212–217.
  11. Sliwa, B., Falkenberg, R., & Wietfeld, C. (2020). Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks. ArXiv, 6–10.
  12. Forward, C., Bandwidth, E., & Access, M. (2020). Machine Learning for 6G Wireless, (September), 2–14.

Legende: [Presenter] Paper