Automatic Modulation Recognition: A Few-Shot Learning Method Based on the Capsule Network

Lixin Li, Junsheng Huang, Qianqian Cheng, Hongying Meng, Zhu Han

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, aiming to obtain higher classification accuracy, DL requires numerous training samples. In order to solve this problem, it is a challenge to study how to efficiently use DL for AMR in the case of few samples. In this letter, inspired by the capsule network (CapsNet), we propose a new network structure named AMR-CapsNet to achieve higher classification accuracy of modulation signals with fewer samples, and further analyze the adaptability of DL models in the case of few samples. The simulation results demonstrate that when 3% of the dataset is used to train and the signal-to-noise ratio (SNR) is greater than 2 dB, the overall classification accuracy of the AMR-CapsNet is greater than 80%. Compared with convolutional neural network (CNN), the classification accuracy is improved by 20%.

Original languageEnglish
Article number9245503
Pages (from-to)474-477
Number of pages4
JournalIEEE Wireless Communications Letters
Volume10
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Convolutional neural network (CNN)
  • automatic modulation recognition (AMR)
  • capsule network (CapsNet)
  • deep learning (DL)
  • few-shot learning

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