Modulation Classification of QAM Signals with Different Phase Noise Levels Using Deep Learning

Hatim Alhazmi, Alhussain Almarhabi, Abdullah Samarkandi, Yu Dong Yao

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

Abstract

Spectrum awareness gained attention to solve the emerging communication problems, e.g., overcoming spectrum shortage and performing precise interference management. Phase noise and fading are well-known concerns in wireless communication, which could result from the channel or thermal noise that worsens the communication systems. Deep learning showed outstanding results in solving communication system problems compared to traditional methods. Our work focuses on automatic modulation classification with different phase noise levels in the fading channel using constellation diagrams as input. Our neural network demonstrates its capability to capture a different modulation format under different phase noise levels with excellent classification accuracy.

Original languageEnglish
Title of host publication2022 31st Wireless and Optical Communications Conference, WOCC 2022
Pages57-61
Number of pages5
ISBN (Electronic)9781665469500
DOIs
StatePublished - 2022
Event31st Wireless and Optical Communications Conference, WOCC 2022 - Shenzhen, China
Duration: 11 Aug 202212 Aug 2022

Publication series

Name2022 31st Wireless and Optical Communications Conference, WOCC 2022

Conference

Conference31st Wireless and Optical Communications Conference, WOCC 2022
Country/TerritoryChina
CityShenzhen
Period11/08/2212/08/22

Keywords

  • Automatic modulation classification
  • Phase noise
  • constellation diagram
  • deep learning
  • fading
  • phase shift keying
  • quadrature amplitude modulation

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