Tutor: | Steffen Gracla, Alea Schröder |
Type of Thesis: | Project (MSc) |
date of issue: | 08/2023 |
Student: | Cedric Sündermann |
Status: | in progress |
Abstract: | 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. Prerequisites: - Understanding of communication fundamentals and mathematical skills - Prior experience with Python programming required - Prior experience with Python machine learning toolkits recommended |