Transfer Learning for Automatic Modulation Recognition Using a Few Modulated Signal Samples

Wensheng Lin, Dongbin Hou, Junsheng Huang, Lixin Li, Zhu Han

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

This letter proposes a transfer learning model for automatic modulation recognition (AMR) with only a few modulated signal samples. The transfer model is trained with the audio signal UrbanSound8K as the source domain, and then fine-tuned with a few modulated signal samples as the target domain. For improving the classification performance, the signal-to-noise ratio (SNR) is utilized as a feature to facilitate the classification of signals. Simulation results indicate that the transfer model has a significant superiority in terms of classification accuracy.

Original languageEnglish
Pages (from-to)12391-12395
Number of pages5
JournalIEEE Transactions on Vehicular Technology
Volume72
Issue number9
DOIs
StatePublished - 1 Sep 2023

Keywords

  • automatic modulation recognition
  • convolutional neural network
  • deep learning
  • few-shot learning
  • Transfer learning

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