OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning

Jiaqi Yu*, Ye Yuan, Qian Zhang, Wei Zhang, Ziyu Fan, Fusheng Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal, OFDM (Orthogonal Frequency Division Multiplexing) signal is of high complexity so emitter identification technology under OFDM is extremely challenging. In this paper, an emitter identification method based on contrastive learning and residual network is proposed. First, according to the particularity of OFDM, we adjust the structure of ResNet and propose a targeted data preprocessing method. Then, some data augmentation strategies are designed to construct positive samples. We conduct self-supervised pretraining to distinguish features of positive and negative samples in hidden space. Based on the pretrained feature extractor, the classifier is no longer trained from scratch. Extensive experiments have validated the effectiveness of our proposed methods.

Original languageEnglish
Article number91
JournalApplied Sciences (Switzerland)
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • ResNet
  • contrastive learning
  • data augmentation
  • emitter identification
  • orthogonal frequency division multiplexing (OFDM)

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