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

1. Remembering: the students will be able to find recent literature on the topic of wireless communications, to work out details independently and to know about potential problems in future Technologies.

2. Understanding: the students will be able to understand existing problems by learning modern transmission techniques.

3. Applying: the students will be able to apply the proposed solutions. They obtain the capability to communicate and present as well as team working.

4. Analysing: the students will be able to analyse proposed solutions by utilizing modern techniques.

5. Synthesising: the students will be able to include proposed solutions in the future cellular networks.

6. Assessing: the students will be able to assess the performance of future techniques, including patenting.

With this the students will be prepared to work within an Austrian or European network operator.

In the mobile communications seminar focused on machine learning's role in enabling future wireless networks, including 5G and beyond, we explore the urgent need for networks to support vastly increased capacity, reduced latency, optimized energy use, and self-awareness. The seminar covers how data-driven solutions can tackle key challenges such as resource allocation, traffic management, network optimization, security, and Quality of Service (QoS) enhancement. Through expert lectures, literature review, and discussions, participants gain insights into the integration of machine learning with wireless technology to meet the evolving demands of modern telecommunications.

In this lecture, we will hear talks on how to reach these goals and conduct self-study in current literature as well as present 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 the 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 on 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 to attend the corresponding seminar over there. Attendance on these trips is required.

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 the 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 (~30 minutes). 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):

[x] ZD Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT

GUMBLE: Uncertainty-Aware Conditional Mobile Data Generation using Bayesian Learning

An End-To-End Analysis of Deep Learning-Based Remaining Useful Life Algorithms for Satefy-Critical 5G-Enabled IIoT Network s

[x] DE  Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

[x] GSE  A new scheduler for URLLC in 5G NR IIoT networks with spatio-temporal traffic correlations

Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning

Distributed resource allocation for URLLC in IIoT scenarios: A multi-armed bandit approach

[x] DE  Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning

[x] ZD Reinforcement Learning-Based Trajectory Planning For UAV-aided Vehicular Communications

[x] SEG Optimizing beam selection and resource allocation in uav-aided vehicular networks

 Energy Optimization in Sustainable Smart Environments With Machine Learning and Advanced Communications

This seminar is targeted for students in the Master of Telecommunications. Note that the seminar requires travelling to Bratislava  and is held entirely in English