Deep-Learning-Based Automatic Modulation Classification Using Imaging Algorithm
Ji-Yeon Park, Dongho Seo, Haewoon Nam · The Journal of Korean Institute of Electromagnetic Engineering and Science · 2021
This paper presents an automatic modulation classification method that involves the application of various imaging algorithms to a convolutional neural network (CNN). The effect of time-series data imaging on the performance of CNN-based modulation classification is analyzed. Our experiment suggests that converting raw signal data into image data using Markov transition field can reduce the error rate of CNN classification from 34 % to 30 % in case of −6 dB signal to noise ratio (SNR) and from 37 % to 18 % in case of 0 dB SNR. This study shows that time-series imaging is a viable preprocessing method for improving the performance of CNN-based modulation classification.