Abstract
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 language | English |
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Article number | 8979431 |
Pages (from-to) | 1042-1046 |
Number of pages | 5 |
Journal | IEEE Communications Letters |
Volume | 24 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2020 May |
Keywords
- CNN
- Detection
- deep learning
- frequency hopping
- hybrid CNN-RNN
ASJC Scopus subject areas
- Modelling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering