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

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 accu-racy at low SNR by achieving 85.5% accuracy at-2 dB SNR. Furthermore, it advances the recog-nition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).