Seminar Wireless Communications
Course No. 389.174 (TISS Link)
2021S, 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.
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.
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
Univ.Prof. Peter Farkas,
Dr. Martin Slanina,
Univ.Prof. Markus Rupp,
+ invited researchers.
Vorbesprechung: Donnerstag. 4.3.2020, 12:30 Uhr, in Zoom
Zoom Link: https://tuwien.zoom.us/j/97241397442?pwd=RDdRL1pLY2tLb1dTem55dEQzRm8xQT09
4.3. Vienna – Meeting point Zoom (https://tuwien.zoom.us/j/97241397442?pwd=RDdRL1pLY2tLb1dTem55dEQzRm8xQT09) @ 13.00
- Introduction to 5G Simulator (Recorded Session), Prof. Kaltenberger (EURECOM)
11.3. Bratislava – Meeting Point Zomm (see above) 13.00 (2h)
- ORAN Access (r)evolution and its potential; Ing. Martin Macuha, PhD. Technology Consulting Manager at Accenture
18.3. Bratislava – Meeting Point Zomm (see above) 13.00 (2h)
- ORAN perspectives Ing. Matú Turcsány, PhD., Chief Technology Officer (Czech, Hungary, Slovakia, Slovenia) Ericsson.
25.3. Vienna – Meeting point Zoom (see above) @ 13.00 (3h)
- 13.00 On the evolution of Fronthaul in Mobile Networks; Elmar Trojer (Ericsson Research)
- Abstract: Radio networks have seen major updates over their generations in providing higher capacity, lower latency, and in supporting more sophisticated use-cases.Best performance in the radio access network and core requires an underlying transport network that can fulfil the need of the radio interfaces and their supported features.The talk will highlight the evolution of the RAN transport segment from basic p2p TDM interfaces in GSM to fully packetized mesh networks including wireless transport components in 5G.
- 14.30LEARNING FROM THE SKY: ROBOT-AIDED MAPPING, RADIO ACCESS AND LOCALIZATION David Gesbert (EURECOM)
- Abstract–The use of flying robots (drones) carrying radio transceiver equipment is the new promising frontier in our quest towards ever more flexible, adaptable and spectrally efficient wireless networks. Beyond obvious challenges within regulatory, control, and battery life, the deployment of autonomous flying radio access network (Fly-RANs) also comes with a number of exciting new research problems at the core of which lies the issue of autonomous real-time placement of the drones in a way that can guarantee user and network performance. We show recent results for this problem in scenarios as diverse as IoT monitoring, mobile broadband access and adhoc connectivity. In this talk we also show how radio-aided autonomous robots can also be used for mapping and user localization purposes. Our approaches lie at the cross-roads between machine learning, signal processing and optimization. Early-stage practical realizations are demonstrated.David Gesbert (IEEE Fellow) is Professor and Head of the Communication Systems Department, EURECOM. Prior to EURECOM, he was with the University of Oslo and before this he was a founding engineer of Iospan Wireless Inc, a Stanford spin off pioneering MIMO-OFDM (now Intel). D. Gesbert has published about 300 papers and 25 patents, some of them winning the 2015 IEEE Best Tutorial Paper Award (Communications Society), 2012 SPS Signal Processing Magazine Best Paper Award, 2004 IEEE Best Tutorial Paper Award (Communications Society), 2005 Young Author Best Paper Award for Signal Proc. Society journals, and paper awards at conferences 2011 IEEE SPAWC, 2004 ACM MSWiM. He has been a Technical Program Co-chair for ICC2017. He was named a Thomson-Reuters Highly Cited Researchers in Computer Science. Since 2015, he holds the ERC Advanced grant “PERFUME” on the topic of smart device Communications in future wireless networks. He is a Board member for the OpenAirInterface (OAI) Software Alliance.
22.4. Brno – Meeting point Zoom (see above) @ 9.30 — 11.30 (2h)
- Everyday Antennas for Sub-6GHz Applications ing. Michal Pokorný, Ph.D. (Resideo)
15.4. Brno – Meeting point Zoom (see above) @ 9.00 — 11.00 (+discussion) (2h)
- RF engineering for advanced satellite telecommunication payloads ing. Václav Valenta, Ph.D. (ESA)
- link for the webinar https://teams.microsoft.com/l/meetup-join/19%3ameeting_YTZiOTQyYzEtMzkyYi00YmY0LTkwYzQtMjY1YmU5ZTM1MWIy%40thread.v2/0?context=%7b%22Tid%22%3a%22dffdfdd4-77f5-445a-9dc0-1f5c6d259395%22%2c%22Oid%22%3a%224eaa5e81-c8c9-464d-9a64-c9c3a949f148%22%7d
Bring your documents.
You might find this helpful: https://www.gcu.ac.uk/library/pilot/researchskills/criticalreviewing/
List of Papers (work in progress):
- [CH] Chen, Y., Lin, X., Khan, T., & Mozaffari, M. (2019). Efficient drone mobility support using reinforcement learning. ArXiv, 2–7.
- 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
- [TP] 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
- [ TP] 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
- [CH] 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
- Gama, F., Isufi, E., Leus, G., & Ribeiro, A. (2020). Graphs, Convolutions, and Neural Networks, (November).
- [AS] 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
- 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
- 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
- [AS] 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.
- Sliwa, B., Falkenberg, R., & Wietfeld, C. (2020). Towards Cooperative Data Rate Prediction for Future Mobile and Vehicular 6G Networks. ArXiv, 6–10.
- [MC] Forward, C., Bandwidth, E., & Access, M. (2020). Machine Learning for 6G Wireless, (September), 2–14.
- [SE] Artificial Intelligence for UAV-enabled Wireless Networks: A Survey + literatur
- [MC] ‘6G Cellular Networks and Connected Autonomous Vehicles’, Jianhua He, Kun Yang and Hsiao-Hwa Chen, 2020
Legende: [Presenter] Paper