Nach positiver Absolvierung der Lehrveranstaltung sind Studierende in der Lage Kenntnisse der aktuellen Forschungsarbeiten und Entwicklungsarbeiten in der Mobilkommunikation, Vorbereitung auf erfolgreiche Tätigkeit bei österreichischer und europäischer Industrie bzw Netzbetreibern.

This year's topic of the mobile communications seminar is focusing on antennas as the enabler for wireless access 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 antennas designs in existing bands and completely new developments for the new spectra of 60GHz. Other new challenges are the support of remote radio locations, radio over fibre relays in moving mass transportation and finding new materials for the antenna itself to reduce the loss at very high frequencies. In this lecture, we will hear talks on how to reach these goals.

alle moderne methoden die gerade diskutiert werden können zur anwendung kommen

Note: 389.075 will not be offered anymore from 2017 on.

Note: Visiting the online course of the partner universities is a mandatorily required element of the seminar, make sure that you are able to join.

Note: This year the seminar will take place online.

The seminar starts in March online.

If you like to participate, please join at the date and contact

philipp.svoboda@tuwien.ac.at

check the homepage

https://www.nt.tuwien.ac.at/teaching/summer-term/389-174/

for recent information

the course is held in English. We will also travel to TU Bratislava as well as to TU Brno to attend the corresponding seminar over there. Attendence on these thrips is required.

Vorbesprechung: Donnerstag. 3.3., 10:30 Uhr, in Zoom

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

See dates for the details of the course

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

In the second part of the seminar, students will read literature and research papers on antenna systems for 5G and beyond and reflect their results by their own presentations. Please choose two paper from our suggested paper list and report to Philipp Svoboda till end of March. 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 until 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

List of Papers (work in progress):

1. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., … Seth, K. (2016). Practical Secure Aggregation for Federated Learning on User-Held Data, (Nips). Retrieved from http://arxiv.org/abs/1611.04482
2. Li, L., Fan, Y., Tse, M., & Lin, K. Y. (2020). A review of applications in federated learning. Computers and Industrial Engineering, 149(September). https://doi.org/10.1016/j.cie.2020.106854
   Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y. C., & Kim, D. I. (2018). Applications of deep reinforcement learning in communications and networking: A survey. ArXiv, 21(4), 3133–3174.
3. Theile, M., Bayerlein, H., Nai, R., Gesbert, D., & Caccamo, M. (2020). UAV coverage path planning under varying power constraints using deep reinforcement learning. ArXiv.
   Zeng, Y., Wu, Q., & Zhang, R. (2019). Accessing from the sky: A tutorial on UAV communications for 5G and beyond. ArXiv, 107(12).
4. Liu, C. H., Dai, Z., Zhao, Y., Crowcroft, J., Wu, D., & Leung, K. K. (2021). Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning. IEEE Transactions on Mobile Computing, 20(1), 130–146. https://doi.org/10.1109/TMC.2019.2938509
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. Bithas, P. S., Michailidis, E. T., Nomikos, N., Vouyioukas, D., & Kanatas, A. G. (2019). A survey on machine-learning techniques for UAV-based communications. Sensors (Switzerland), 19(23), 1–39. https://doi.org/10.3390/s19235170
7. Gupta, A., Ghanshala, K., & Joshi, R. C. (2021). Machine learning classifier approach with gaussian process, ensemble boosted trees, svm, and linear regression for 5g signal coverage mapping. International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), 156–163. https://doi.org/10.9781/ijimai.2021.03.004
8. Chandar, S., Mansoor, M., Ahmadi, M., Badve, H., Sahoo, D., & Katragadda, B. (2020). Machine Learning Based Network Coverage Guidance System. Retrieved from http://arxiv.org/abs/2010.13190
9. Rozenblit, O., Haddad, Y., Mirsky, Y., & Azoulay, R. (2018). Machine learning methods for SIR prediction in cellular networks. Physical Communication, 31, 239–253. https://doi.org/10.1016/j.phycom.2018.08.005
10. Re Calegari, G., Carlino, E., Peroni, D., & Celino, I. (2016). Filtering and windowing mobile traffic time series for territorial land use classification. Computer Communications, 95, 15–28. https://doi.org/10.1016/j.comcom.2016.04.016
11. Michelmore, R., Wicker, M., Laurenti, L., Cardelli, L., Gal, Y., & Kwiatkowska, M. (2020). Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control. Proceedings - IEEE International Conference on Robotics and Automation, 7344–7350. https://doi.org/10.1109/ICRA40945.2020.9196844
12. Band, N., Nado, Z., Dusenberry, M., & Gal, Y. (2021). Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks, (NeurIPS).
13. Nado, Z., Band, N., Collier, M., Djolonga, J., Dusenberry, M. W., Farquhar, S., … Tran, D. (2021). Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning, 1–13. Retrieved from http://arxiv.org/abs/2106.04015
14. Gal, Y., & Ghahramani, Z. (2015). Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, 1–12. Retrieved from http://arxiv.org/abs/1506.0215

This seminar is targeted for students in the Master of telecommunications. Note that the seminar requires travelling to bratislava and brno and is held entirely in english