Modulation Classification Based on Eye Diagrams and Deep Learning

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

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

3 Scopus citations

Abstract

New emerging technologies such as the Internet of Things (IoT) and fifth generation wireless communication New Radio (5G NR) are introducing challenges in spectrum and systems' complexity. Radio spectrum awareness is overcoming many challenges through tasks related to signal detection and channel identification that improves overall the system's reliability, efficiency, and security. The eye diagram is one of the signal representations useful in many applications for simulation and debugging of the system. The eye diagram shows vital parameters such as timing jitter and inter-symbol interference. The eye diagram contains essential features that could be used for spectrum awareness tasks. This paper uses deep learning with an eye diagram to study and identify modulated signals in narrowband fading channels, e.g., Rayleigh and Rician fading. Our results show that deep learning neural networks can classify modulated signals with the impact of the fading channel using an eye diagram.

Original languageEnglish
Title of host publication2022 31st Wireless and Optical Communications Conference, WOCC 2022
Pages35-40
Number of pages6
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
  • deep learning
  • eye-diagram
  • internet of things
  • narrow-band
  • spectrum awareness

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