Detection of frequency-hopping signals with deep learning

Kyung Gyu Lee, Seong Jun Oh

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Detection of the frequency-hopping (FH) signal is challenging when the hopping rate is unknown. Conventional spectrogram-based schemes can detect FH signals, but its performance is limited by the time-frequency resolution trade-off and spectral leakage. To alleviate this issue, we design convolutional neural network (CNN) and hybrid CNN/recurrent neural network (RNN)-based schemes. The CNN-based scheme alleviates spectral leakage by using feature maps. The hybrid CNN/RNN-based scheme mitigates the time-frequency resolution trade-off by using feature maps extracted from spectrograms of various window lengths. In simulations, the hybrid CNN/RNN-based scheme is shown to outperform the CNN-based and conventional detection schemes.

Original languageEnglish
Article number8979431
Pages (from-to)1042-1046
Number of pages5
JournalIEEE Communications Letters
Issue number5
Publication statusPublished - 2020 May


  • CNN
  • Detection
  • deep learning
  • frequency hopping
  • hybrid CNN-RNN

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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