A hybrid model for automatic modulation classification based on residual neural networks and long short term memory

• Published in: Alexandria engineering journal Vol. 67; pp. 117 - 128
• Main Authors: Elsagheer, Mohamed M., Ramzy, Safwat M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-03-2023; Elsevier
• Subjects: AMC; Deep learning; LSTM; Residual neural network; SNR; Deep learning; FC layer; CNN; Residual neural network; LSTM; SCRNN; SNR; AMC; CLDNN; ResNet
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2022.08.019

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.