After successful completion of the course, students are able to apply Knowledge of current research and development work in mobile communication, preparation for successful work in Austrian and European industry or network operators.

List of skills:

Ability to communicate and collaborate: Participants will enhance their communication and collaboration skills through team-based activities, discussions, and presentations.

 Ability to critically evaluate wireless communication research: Participants will learn how to critically evaluate wireless communication research, including research papers, patents, and industry reports.

Awareness of emerging wireless communication trends: Participants will be introduced to emerging trends and challenges in wireless communication, such as the Internet of Things (IoT), wireless security, 5G, 6G, machine learning and spectrum sharing.

This year's topic of the mobile communications seminar is focusing on machine learning as the enabler for wireless access networks of the future. 5G and beyond networks are expected to be significantly different from today's networks. The main requirements are: to 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 require data driven solutions in order so solve: Resource allocation optimization, Traffic prediction and management, Network optimization and management, Security and privacy, and Quality of Service (QoS) monitoring and optimization.

In this lecture, we will hear talks on how to reach these goals and conduct self study in current literature as well as presenting the condensed papers knowledge.

The teaching methods used in a seminar may vary depending on the current topic. The common teaching methods used in this seminar are:

Lecture: In a seminar, the lecturer presents information and concepts to the participants.

Discussion: Discussions are a key component of seminars. They allow participants to share their ideas, ask questions, and explore different perspectives.

Case studies: Case studies are a valuable teaching method in seminars, as they provide participants with real-life examples of concepts and theories. Participants can analyze and discuss the case study as a whole and ask the experts for feedback.

Group activities: Group activities such as brainstorming, problem-solving, and travel activities help participants apply the concepts they have learned in the seminar to real-life situations. Group activities can also enhance teamwork and communication skills.

Presentations: Participants can be given the opportunity to present their ideas, research, or projects to the seminar group. This allows participants to develop their public speaking skills and receive feedback from their peers.

Overall, only most modern methods that are in the current discussion are being applied.

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

Note: Visiting the 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. 2.3., 11:00 Uhr, in Person: EI 5, with presentation from Ke Guan

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 beyond 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 on their results through their own presentations. Please choose two papers from our suggested paper list and report to Philipp Svoboda by 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.

Attendance of the seminar is compulsory! We will keep records of your attendance. The seminar starts with invited talks, and 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!) (~15 pages).

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. C. Zhang, X. Sun, W. Xia, J. Zhang, H. Zhu and X. Wang, "Deep Learning Based Double-Contention Random Access for Massive Machine-Type Communications," in IEEE Transactions on Wireless Communications, 2022, doi: 10.1109/TWC.2022.3206769.
2. N. Yarkina, A. Gaydamaka, D. Moltchanov and Y. Koucheryavy, "Performance Assessment of an ITU-T Compliant Machine Learning Enhancements for 5 G RAN Network Slicing," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2022.3228286.
3. I. F. Mohamad Rafie, S. Y. Lim and M. J. H. Chung, "Path Loss Prediction in Urban Areas: a Machine Learning Approach," in IEEE Antennas and Wireless Propagation Letters, doi: 10.1109/LAWP.2022.3225792.
4. Z. Liu, L. Chen, X. Zhou, Z. Jiao, G. Guo and R. Chen., "Machine Learning for Time-of-Arrival Estimation with 5G Signals in Indoor Positioning," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3234123.
5. T. S. Cousik, V. K. Shah, T. Erpek, Y. E. Sagduyu and J. H. Reed, "Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mm Wave Networks," in IEEE Transactions on Network Science and Engineering, 2022, doi: 10.1109/TNSE.2022.3201748.
6. A. Balieiro, K. Dias and P. Guarda, "A Machine Learning Approach for CQI Feedback Delay in 5G and Beyond 5G Networks," 2021 30th Wireless and Optical Communications Conference (WOCC), Taipei, Taiwan, 2021, pp. 26-30, doi: 10.1109/WOCC53213.2021.9603019.
7. T. Wild, V. Braun and H. Viswanathan, "Joint Design of Communication and Sensing for Beyond 5G and 6G Systems," in IEEE Access, vol. 9, pp. 30845-30857, 2021, doi: 10.1109/ACCESS.2021.3059488.
8. J. Park et al., "Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications," in Proceedings of the IEEE, vol. 109, no. 5, pp. 796-819, May 2021, doi: 10.1109/JPROC.2021.3055679.
9. G. Zhu, J. Xu, K. Huang and S. Cui, "Over-the-Air Computing for Wireless Data Aggregation in Massive IoT," in IEEE Wireless Communications, vol. 28, no. 4, pp. 57-65, August 2021, doi: 10.1109/MWC.011.2000467.
10. R. Ali, Y. B. Zikria, A. K. Bashir, S. Garg and H. S. Kim, "URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence," in IEEE Access, vol. 9, pp. 67064-67095, 2021, doi: 10.1109/ACCESS.2021.3073806.
11. S. Huang, Y. Ye, M. Xiao, H. V. Poor and M. Skoglund, "Decentralized Beamforming Design for Intelligent Reflecting Surface-Enhanced Cell-Free Networks," in IEEE Wireless Communications Letters, vol. 10, no. 3, pp. 673-677, March 2021, doi: 10.1109/LWC.2020.3045884.
12. D. M. Manias and A. Shami, "Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems," in IEEE Network, vol. 35, no. 3, pp. 88-94, May/June 2021, doi: 10.1109/MNET.011.2000552.
13. B. Brik, K. Boutiba and A. Ksentini, "Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges," in IEEE Open Journal of the Communications Society, vol. 3, pp. 228-250, 2022, doi: 10.1109/OJCOMS.2022.3146618.
14. A. Jagannath, J. Jagannath and T. Melodia, "Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning With Deep Unfolding," in IEEE Transactions on Artificial Intelligence, vol. 2, no. 6, pp. 528-536, Dec. 2021, doi: 10.1109/TAI.2021.3108129.

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