Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neura...

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Bibliographic Details
Published in:Computers (Basel) 2022-11, Vol.11 (11), p.162
Main Authors: Abdulkarem, Ahmed Mohammed, Abedi, Firas, Ghanimi, Hayder M. A., Kumar, Sachin, Al-Azzawi, Waleed Khalid, Abbas, Ali Hashim, Abosinnee, Ali S., Almaameri, Ihab Mahdi, Alkhayyat, Ahmed
Format: Article
Language:English
Subjects:
Algorithms
Amplitudes
Artificial neural networks
Classification
Continuous wavelet transform
Data transmission
Deep learning
Digital transmission
Machine learning
Methods
Military communications
Modulation
Modulation (Electronics)
multiclass classification
Neural networks
Noise levels
Quadrature phase shift keying
Satellite communications
Signal classification
Signal processing
Time-frequency analysis
Underwater communication
wavelet transform
Wavelet transforms
Wireless communications
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Summary:This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels.
ISSN:2073-431X
2073-431X
DOI:10.3390/computers11110162