Machine Learning based Baseband Processing for Non-Terrestrial Networks

Tutor: Tim Düe
Type of Thesis: Project (MSc), Master's thesis (MSc), Bachelor thesis (BSc.)
date of issue: 04/2024
Student: -
Status: available


One of the approaches to provide the ubiquitous connectivity 6G aims for is the use of Non-Terrestrial Networks (NTNs). These NTNs expand our traditional terrestrial communication networks, by adding airborne and spaceborne platforms, which are not influenced by terrestrial hazards, such as earthquakes and other catastrophies, and cover wide areas. However, they also come with a set of new challanges, such as increased Doppler spread due to the satellites fast movement and long delays due to the large distance. In recent years, a number of Machine Learning (ML) based solutions have shown to deliver very good results for terrestrial use cases. Your task in this project/thesis is to investigate the applicability of these solutions for NTN use cases and find good solutions for NTN scenarios.


The final objectives will be agreed on together with the student, the following is only a suggestion.

  • Investigate the applicability of existing ML based solutions for terrestrial networks in NTN scenarios
  • Develop promissing ML based approaches for NTN


Ideally you fullfill the following criteria. If your profile varies but you are still interested in the project, you may contact me anyways.

  • Solid programming skills in Python
  • Ideally experience with Tensorflow
  • Basic knowledge of ML
  • Fundamental knowledge of baseband processing in communication systems

More Information:

For more information, feel free to contact me via E-Mail (

Last change on 15.04.2024 by T. Düe
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