Automatic Modulation Classification Using Compressive Convolutional Neural Network

Sai Huang, Lu Chai, Zening Li, Di Zhang, Yuanyuan Yao, Yifan Zhang, Zhiyong Feng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)


The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.

Original languageEnglish
Article number8734711
Pages (from-to)79636-79643
Number of pages8
JournalIEEE Access
Publication statusPublished - 2019


  • Automatic modulation classification
  • compressive loss constraint
  • deep convolutional neural network
  • multiple constellation images

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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