AI-enabled wireless networks (WS 2021/22)

Details

Kind of event Lecture (2 SWS)
ECTS Credits 2,5
Languange Englisch
Lecture Friday, 3:00 pm – 4:30 pm, room 00.151-113 UnivIS , online if necessary

This course introduces machine learning algorithms such as supervised, unsupervised, reinforcement, deep, and federated learning and their application in the next generation wireless and mobile networks. Different ML use cases are explained which solve problems in different layers of the protocol stack from the physical layer to the application layer. The course includes the following topics:
    1. Introduction to machine learning algorithms
    2. Python programming language and its ML tools
    3. AI-enabled wireless and mobile networks
    3.1 Cellular networks and ML use cases
    3.1.1 History of 2G to 4G, 5G and 6G vision
    3.1.2 ML use cases in physical, MAC and higher layers
    3.2 5G-V2X (cellular-V2X) and ML use cases
    3.2.1 Sidelink communication as the key enabler
    3.2.2 5G-V2X features and use cases
    3.2.3 ML use cases in 5G-V2X
    3.3 Intelligent wireless networks
    3.3.1 Cognitive radio networks
    3.3.2 ML use case in wireless networks
    4. Standardization activities on AI-enabled wireless networks
    4.1.1 3GPP and 5GAA
    4.1.2 ETSI Zero touch networks

Exercises:
Literature review on the application of machine learning in wireless networks
The exercise of this course includes a literature review research project where students work individually on a relevant topic. The steps to accomplish the research project are as follows:

    A. Select a topic relevant to the application of ML in wireless networks and register it by email
    B. Search for the relevant papers and make a list of papers
    C. Study the papers and prepare a summary
    D. Present the outcomes

Each student should present her/his research study in an intermediate and a final presentation. A summary paper should be written following the “survey papers guideline” using IEEE format.
The grade of the research project will be considered as a “Bonus point” (up to 20%) for the final grade.

  • Dahlman, Erik, Stefan Parkvall, and Johan Skold. 5G NR: The next generation wireless access technology. Academic Press, 2020.
  • Sun, Yaohua, et al. “Application of machine learning in wireless networks: Key techniques and open issues.” IEEE Communications Surveys and Tutorials 21.4 (2019): 3072-3108.
  • Harounabadi, Mehdi, et al. “V2X in 3GPP Standardization: NR Sidelink in Release-16 and Beyond.” IEEE Communications Standards Magazine 5.1 (2021): 12-21.
  • Xie, Junfeng, et al. “A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges.” IEEE Communications Surveys and Tutorials 21.1 (2018): 393-430.