Research on Automatic Modulation Recognition of Medium-SNR Signals Based on Deep Learning

With the rapid development of electronic technology and increasing requirements on signal utilization efficiency and low bit error rates, diverse signal modulation schemes have emerged to accommodate diverse scenarios, including electronic countermeasures and military competition. The growing complexity of modulation schemes has made modulation recognition increasingly challenging, positioning deep learning-based automatic modulation recognition as a major research focus. This study uses the RadioML 2018.01A dataset from Kaggle to evaluate modulation recognition performance under signal-to-noise ratios ranging from 0 dB to 12 dB. A conventional convolutional neural network (CNN) and a residual neural network (ResNet), both commonly employed in image classification, are adopted and compared. The objective is to identify the modulation scheme used by the signals in the dataset and classify them into the corresponding categories. For certain modulation schemes, such as 64QAM and 128QAM, the residual neural network achieves classification accuracies that are up to approximately 20% higher than those of the convolutional neural network. Although the recognition accuracy of ResNet for some modulation schemes is slightly lower than that of the CNN, it remains within an acceptable range. Experimental results demonstrate that under medium signal-to-noise ratio conditions, the residual neural network outperforms the convolutional neural network.