Digital Signal Modulation Classification using Data Conversion Method based on Convolutional Neural Network

Abstract

Automated modulation classification (AMC) plays a very important part in cognitive electronic warfare. Deep learning (DL) is a new machine learning (ML) method, which has been successfully implemented in many application fields. This paper proposes a new data conversion algorithm in order to gain a better classification accuracy of target signal modulation. Then a Convolutional Neural Network (CNN) architecture is developed, and its performance is proved to be better than the traditional data conversion method. In addition, the impacts of representation on classification performance are also analysed. Experimental results demonstrate the significant performance advantage of the proposed data conversion method using DL-based approach for modulation classification.