Channel Estimation using Generative Adversarial Networks and Machine Learning

Tutor: Matthias Hummert
Type of Thesis: Project (MSc)
date of issue: 12/2020
Student: Ariel Theophane Dzoanda Watat
Status: in progress


Channel estimation is one of the fundamental problems of communication technologies. Often the channel estimate is based on a specific model which has the drawback that influences that are not incorporated in the model are not picked up by the estimation. To overcome this drawback we can think of a data driven approach which learns the underlying channel purely by given data.


The aim of this thesis is to estimate the channel with a deep learning approach named Generative Adversarial Networks. To realize this, a fixed transmitter should be implemented and measurements should be made at the receiver side. Afterwards the Generative Adversarial Network is trained to learn a channel estimation using these measurements.


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

Last change on 11.12.2020 by M. Hummert
AIT ieee GOC tzi ith Fachbereich 1
© Department of Communications Engineering - University of BremenImprint / Contact