Modulation Classification in a Multipath Fading Channel Using Deep Learning: 16QAM, 32QAM and 64QAM

Abdullah Samarkandi, Alhussain Almarhabi, Hatim Alhazmi, Mofadal Alymani, Mohsen H. Alhazmi, Yu Dong Yao

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

3 Scopus citations

Abstract

A method based on a constellation diagram is proposed to identify QAM modulation of different orders in static, slow, and frequency selective fading channels. Although constellation diagrams have been studied and classified in literature, most of the work focused on noise. Little has been done to study the effect of multipath fading channels. We develop a highly accurate modulation classification method by exploiting deep learning with the constellation diagram. Based on the experimental results, our CNN model achieves a classification accuracy of 100% at -10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel.

Original languageEnglish
Title of host publication2021 30th Wireless and Optical Communications Conference, WOCC 2021
Pages6-10
Number of pages5
ISBN (Electronic)9781665427722
DOIs
StatePublished - 2021
Event30th Wireless and Optical Communications Conference, WOCC 2021 - Taipei, Taiwan, Province of China
Duration: 7 Oct 20218 Oct 2021

Publication series

Name2021 30th Wireless and Optical Communications Conference, WOCC 2021

Conference

Conference30th Wireless and Optical Communications Conference, WOCC 2021
Country/TerritoryTaiwan, Province of China
CityTaipei
Period7/10/218/10/21

Keywords

  • Rayleigh fading
  • Spectrum awareness
  • constellation
  • deep learning
  • modulation classification
  • quadrature amplitude modulation (QAM)

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