Deep learning-based modulation recognition with constellation diagram: A case study

Abstract

Automatic modulation recognition is a promising solution for identifying and classifying signals received in heterogeneous wireless networks. In dynamic and autonomous environments, receivers must extract the relevant signal from various modulated signals to enable further communication procedures. Machine learning, including its sub-branches for classification problems, offers promising operational capabilities. This study utilized the ResNet-50 deep learning method for modulation classification. A dataset consisting of eight digital modulation techniques was generated, with constellation diagrams created as image data over the additive white Gaussian noise (AWGN) channel at signal-to-noise ratios (SNR) of 5 dB, 10 dB, and 20 dB. The deep learning algorithm's performance metrics were evaluated using a confusion matrix, and F1 scores were compared to those of the AlexNet deep learning algorithm. The simulation results clearly indicate the superior performance of ResNet-50 over AlexNet. In terms of average F1 scores, ResNet-50 exhibits a significant advantage, surpassing AlexNet by approximately 67%, 29%, and 10% at SNR values of 5 dB, 10 dB, and 20 dB, respectively.