Modulation Recognition of Digital Signal Based on Deep Auto-Ancoder Network

Automated Modulation Classification (AMC) shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. A useful digital signal modulation recognition scheme inspired by the deep auto-encoder network is proposed in this investigation. In our pro...

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Bibliographic Details
Published in:	2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) pp. 256 - 260
Main Authors:	Tu Ya, Yun Lin, Hui Wang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.07.2017
Subjects:	Algorithm design and analysis Classification algorithms cyclic feature deep autoencoder network Feature extraction Modulation Modulation recognition Signal processing algorithms Signal to noise ratio Wavelet transforms
Online Access:	Get full text
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Summary:	Automated Modulation Classification (AMC) shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. A useful digital signal modulation recognition scheme inspired by the deep auto-encoder network is proposed in this investigation. In our proposed method, there are two deep auto-encoder networks. The system extracts the original features of the signal one by one to complete the recognition of unknown modulation signals, according to different modulation signals has different cyclic spectrum characteristics and wavelet characteristics. Finally, the effectiveness of the system is verified by simulation. The system can identify nine typical signals, which are 2FSK, 4FSK, 8FSK, BPSK, QPSK, 16QAM, 64QAM, 2ASK, MSK. The recognition accuracy can achieve 85% when signal to noise ratio is higher than 0 dB. The results indicate that digital signal modulation recognition based on deep auto-encoder network is feasible and accuracy rate.
DOI:	10.1109/QRS-C.2017.50