Deep Learning based End-to-End Communication Systems without a Channel Model

Tutor: Edgar Beck
Type of Thesis: Master's thesis (MSc)
date of end: 02/2020
Student: Kristina Kr├╝ger
Status: finished


Motivated by the fact that the innovative machine learning based autoencoder design of communication systems requires a well-known system model, model-free approaches for deployment in adaptive and real-time applications are of interest. Some recent ideas comprise channel estimation using Variational Generative Adversarial Networks and reinforcement learning for training of a communication system.


The aim of this thesis is to investigate one approach to overcome the mentioned limitation and to implement it in a modern deep learning library. In the end, the performance should be evaluated in practice with real data on a software defined radio platform.


In order to work on this thesis, a successful participation in the lectures Wireless Communications and Communication Technologies is required. Also, a solid understanding in linear algebra, optimization theory and stochastics is needed. Programming skills in Python are advantageous but not necessary.

Last change on 26.02.2020 by E. Beck
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