Satellite Precoding through Reinforcement Learning

Tutor: Steffen Gracla, Alea Schröder
Type of Thesis: Project (MSc)
date of issue: 08/2023
Student: Cedric Sündermann
Status: in progress

LEO satellites enable close to global communications coverage and are a key technology for future communication systems. When combining SDMA and precoding, we increase spectral efficiency while managing Inter-User-Interference. Due to the high velocities of LEO satellites and positioning imperfections, finding the optimal precoder can prove challenging.

Model-free reinforcement learning is a technique that enables finding approximately optimal solutions for complex decision problems without any a-priori assumptions on the structure of the problem, instead learning through "trial-and-error".

It has recently proven it's capability to perform on highly demanding tasks.

We offer a selection of projects to study the suitability of combining reinforcement learning and precoding for satellite communications.


- Understanding of communication fundamentals and mathematical skills

- Prior experience with Python programming required 

- Prior experience with Python machine learning toolkits recommended 

Last change on 24.11.2023 by S. Gracla
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