Amadou Tall, Alexander A. Berian, Tamal Bose

Research output: Contribution to journalConference articlepeer-review


Modulation Classification (MC) is an increasingly relevant design feature in wireless communications and plays an essential part in cognitive radio networks. Deep learning methods are decisive in performing MC. MC methods based on constellation diagrams usually achieve excellent performance because of the constellation diagrams' discriminative characteristics. This paper uses deep learning models to classify generated signals' constellation diagrams by their modulation type. We propose a constellation diagram-based MC architecture that uses different training and testing resolutions to classify the modulations of the RadioML dataset. The observed improvement of the classification accuracy relies on the fact that a lower training resolution improves the classification at test time. We also perform a comparative analysis of the model by examining the impact on the classification accuracy when different levels of resolution are applied.

Original languageEnglish (US)
Pages (from-to)300-309
Number of pages10
JournalProceedings of the International Telemetering Conference
StatePublished - 2022
Event57th Annual International Telemetering Conference, ITC 2022 - Glendale, United States
Duration: Oct 24 2022Oct 27 2022


  • CNN
  • Constellation diagrams
  • accuracy
  • modulation classification
  • resolution fixing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing


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