VHF radio signal modulation classification based on convolution neural networks

National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan, Hubei, 430033, China

^a Corresponding author: wowhow@163.com

Abstract

Deep learning architecture has been attracting increasing attention due to the successful applications in various fields. However, its application in radio system has not been well explored. In this paper, we consider the very high frequency (VHF) radio signal modulation classification based on convolution neural networks (CNN). The main principle of CNN is analysed and a five-layer CNN model is built. The proposed CNN-based modulation classification method is proved useful for simulated radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR. In addition, the proposed CNN-based method is used for real VHF radio signals, and the key factors effecting the classification accuracy are analysed.