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

Betreuer: Edgar Beck
Art der Arbeit: Masterarbeit (MSc)
Arbeit beendet: 02/2020
Bearbeiter: Kristina Krüger
Status: abgeschlossen


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.

Zuletzt aktualisiert am 26.02.2020 von E. Beck
AIT ieee GOC tzi ith Fachbereich 1
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