Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

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Abstract

With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.

Original languageEnglish
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
PublisherIEEE
ISBN (Electronic)9781665494557
ISBN (Print)9781665494564
DOIs
Publication statusPublished - 2022
Publication typeA4 Article in conference proceedings
EventIEEE International Workshop on Signal Processing Advances in Wireless Communication - Oulu, Finland
Duration: 4 Jul 20226 Jul 2022

Publication series

NameSPAWC
ISSN (Print)1948-3244
ISSN (Electronic)1948-3252

Conference

ConferenceIEEE International Workshop on Signal Processing Advances in Wireless Communication
Country/TerritoryFinland
CityOulu
Period4/07/226/07/22

Keywords

  • 5G NR
  • 6G
  • deep learning
  • hardware impairments
  • IQ imbalance
  • machine learning
  • mmWave
  • nonlinear distortion
  • phase noise
  • power amplifier
  • sub-THz

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

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