Realizing a communication system with Deep Learning and Hardware

Tutor: Matthias Hummert
Type of Thesis: Project (MSc)
date of end: 04/2021
Student: Konstantin Gei├činger
Status: finished
ANT-shelfmark:
Abstract:

Motivation:

The recent breakthroughs in Deep Learning lead to Artificial Intelligence that is able to beat human performance in games like e.g. chess. The question is now if we are able to transfer these successes to the communication technology world and how it compares to other existing schemes.

Goal:

The aim of this thesis is to apply deep learning techniques to a communication chain consisting of a whole transmitter and receiver chain and an over-the-air interface with hardware. The transmitter and/or receiver side should be realized with deep learning and compared to other existing baseline schemes.

Requirements:

In order to process this thesis, knowledge of the lectures Wireless Communications Technologies, Communication Technologies, Channel Coding and programming skills in Python or Matlab and GNU Radio are essential.

Last change on 21.04.2021 by M. Hummert
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