Combined Signal Representations for Modulation Classification Using Deep Learning: Ambiguity Function, Constellation Diagram, and Eye Diagram

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

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

2 Scopus citations

Abstract

We exploit deep learning convolutional neural networks (CNN) based on joint image representation and propose an automatic modulation classification algorithm to classify the communication signals. The combined representations include a constellation diagram, an ambiguity function (AF), and an eye diagram. Experimentation results show that combining constellation and eye diagrams achieves superior classification performance compared to having these representations separately. Combining AF and an eye diagram results in improvement at low SNR.

Original languageEnglish
Title of host publication32nd Wireless and Optical Communications Conference, WOCC 2023
ISBN (Electronic)9798350337150
DOIs
StatePublished - 2023
Event32nd Wireless and Optical Communications Conference, WOCC 2023 - Newark, United States
Duration: 5 May 20236 May 2023

Publication series

Name32nd Wireless and Optical Communications Conference, WOCC 2023

Conference

Conference32nd Wireless and Optical Communications Conference, WOCC 2023
Country/TerritoryUnited States
CityNewark
Period5/05/236/05/23

Keywords

  • Spectrum awareness
  • ambiguity function
  • constellation diagram
  • deep learning
  • eye diagram
  • modulation classification
  • quadrature amplitude modulation (QAM)

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