Classification of QPSK Signals with Different Phase Noise Levels Using Deep Learning

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

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

7 Scopus citations

Abstract

Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.

Original languageEnglish
Title of host publication2020 29th Wireless and Optical Communications Conference, WOCC 2020
ISBN (Electronic)9781728161242
DOIs
StatePublished - May 2020
Event29th Wireless and Optical Communications Conference, WOCC 2020 - Newark, United States
Duration: 1 May 20202 May 2020

Publication series

Name2020 29th Wireless and Optical Communications Conference, WOCC 2020

Conference

Conference29th Wireless and Optical Communications Conference, WOCC 2020
Country/TerritoryUnited States
CityNewark
Period1/05/202/05/20

Keywords

  • Phase noise
  • constellation diagram
  • deep learning
  • phase shift keying

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