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http://dx.doi.org/10.6109/jkiice.2021.25.3.427

Performance analysis in automatic modulation classification based on deep learning  

Kang, Jong-Jin (C4I R&D Center, Hanwha Systems)
Kim, Jae-Hyun (Department of Electrical and Computer Engineering, Ajou University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.3, 2021 , pp. 427-432 More about this Journal
Abstract
In this paper, we conduct performance analysis in automatic modulation classification of unknown communication signal to identify its modulation types based on deep neural network. The modulation classification performance was verified using time domain digital sample data of the modulated signal, frequency domain data to which FFT was applied, and time and frequency domain mixed data as neural network input data. For 11 types of analog and digitally modulated signals, the modulation classification performance was verified in various SNR environments ranging from -20 to 18 dB and reason for false classification was analyzed. In addition, by checking the learning speed according to the type of input data for neural network, proposed method is effective for constructing an practical automatic modulation recognition system that require a lot of time to learn.
Keywords
Automatic modulation classification; Deep learning; Neural network; SNR; FFT;
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