Adaptive Channel Estimation based on Deep Learning

Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

Original languageEnglish (US)
Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194844
StatePublished - Nov 2020
Event92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
Duration: Nov 18 2020 → …

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
CityVirtual, Victoria
Period11/18/20 → …


  • Adaptive channel estimation
  • Deep learning
  • Machine learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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