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A hybrid model for automatic modulation classification based on residual neural networks and long short term memory.
- Source :
- Alexandria Engineering Journal; Mar2023, Vol. 67, p117-128, 12p
- Publication Year :
- 2023
-
Abstract
- This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier's performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 11100168
- Volume :
- 67
- Database :
- Supplemental Index
- Journal :
- Alexandria Engineering Journal
- Publication Type :
- Academic Journal
- Accession number :
- 162388968
- Full Text :
- https://doi.org/10.1016/j.aej.2022.08.019