Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior

Tomer Fireaizen, Gal Metzer, Dan Ben-David, Yair Moshe, Israel Cohen, Emil Bjornson

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


An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538683477
StatePublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of


  • deep neural prior
  • Intelligent reflecting surface (IRS)
  • OFDM
  • passive beamforming
  • Reconfigurable Intelligent Surface (RIS)


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