Generative-AI Methods for Channel Impulse Response Generation

Franz Weisser, Timo Mayer, Bessem Baccouche, Wolfgang Utschick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this work, we propose methods for generating and manipulating channel impulse responses using normalizing flows. Using standardised, simplified, analytic models, when no perfect description of the channel is known, can lead to performance losses. We are able to show using simulations that our machine learning methods generate channel impulse responses with forced features. In addition to that, we show how disentanglement in the latent space of a normalizing flow can be used for the changing of certain features. We evaluate our methods using the maximum mean discrepancy.

Original languageEnglish
Title of host publicationWSA 2021 - 25th International ITG Workshop on Smart Antennas
PublisherVDE VERLAG GMBH
Pages65-70
Number of pages6
ISBN (Electronic)9783800756889
StatePublished - 2021
Event25th International ITG Workshop on Smart Antennas, WSA 2021 - French Riviera, France
Duration: 10 Nov 202112 Nov 2021

Publication series

NameWSA 2021 - 25th International ITG Workshop on Smart Antennas

Conference

Conference25th International ITG Workshop on Smart Antennas, WSA 2021
Country/TerritoryFrance
CityFrench Riviera
Period10/11/2112/11/21

Keywords

  • Channel impulse responses
  • Generative neural networks
  • Latent space disentanglement
  • Normalizing flows

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