VHF radio signal modulation classification based on convolution neural networks

Hao Wu,Jin Meng,Liang Zhou,Qing Wang,X Lei,Y Yu,S Cai,H Li

doi:10.1051/matecconf/201824603032

Hao Wu, Jin Meng + Show 6 more

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https://doi.org/10.1051/matecconf/201824603032

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Journal: MATEC Web of Conferences	Publication Date: Jan 1, 2018
Citations: 5	License type: CC BY 4.0

Affiliation: Naval University of Engineering

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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.

Highlights

Automatic modulation classification (AMC) has been a hot research topic of communication system in many years
In [11], a modulation recognition algorithm based on antinoise processing and deep sparse-filtering convolutional neural network (AN-SF-convolution neural networks (CNN)), is proposed for modulation classification of very high frequency (VHF) radio signals
The proposed CNN model consists of five layers and is proved useful for simulated VHF radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR

Summary

Introduction

Automatic modulation classification (AMC) has been a hot research topic of communication system in many years. In [8], the authors propose a DL architecture based on four-layer convolutional neural networks for modulation recognition. In [9], three convolutional neural networks are established based on temporal IQ data, amplitude/phase data and frequency domain data, respectively, and the proposed method can reach modulation classification accuracy of 95% even in low SNR. In [11], a modulation recognition algorithm based on antinoise processing and deep sparse-filtering convolutional neural network (AN-SF-CNN), is proposed for modulation classification of VHF radio signals. The proposed CNN model consists of five layers and is proved useful for simulated VHF radio signals generated by MATLAB, that the overall classification accuracy is high even in low SNR.

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