Two-sample tests for validating the UL-DL conjecture in FDD systems

Valentina Rizzello, Nurettin Turan, Michael Joham, Wolfgang Utschick

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

5 Scopus citations

Abstract

In this work, we present a two-sample tests analysis based on the maximum mean discrepancy metric to validate the recently proposed uplink-downlink conjecture for frequency division duplex systems. This novel concept shows that a neural network trained with uplink channel data can adequately generalize to downlink channel data. With this paper, we focus on a particular application of this idea, namely an autoencoder neural network, which has been introduced lately to generate channel feedback, without requiring any training effort at the mobile terminals. Simulation results with several datasets demonstrate that application-based low-dimensional representations for two-sample testing give a deeper insight into the similarities and dissimilarities between the uplink and downlink data distributions and are in accordance with the performance of the neural network that is applied to the respective datasets.

Original languageEnglish
Title of host publication2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021
PublisherVDE VERLAG GMBH
ISBN (Electronic)9781728174327
DOIs
StatePublished - 6 Sep 2021
Event17th International Symposium on Wireless Communication Systems, ISWCS 2021 - Berlin, Germany
Duration: 6 Sep 20219 Sep 2021

Publication series

NameProceedings of the International Symposium on Wireless Communication Systems
Volume2021-September
ISSN (Print)2154-0217
ISSN (Electronic)2154-0225

Conference

Conference17th International Symposium on Wireless Communication Systems, ISWCS 2021
Country/TerritoryGermany
CityBerlin
Period6/09/219/09/21

Keywords

  • Autoencoder neural networks
  • Deep learning
  • FDD systems
  • Machine learning
  • Maximum mean discrepancy

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