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http://dx.doi.org/10.6109/jkiice.2019.23.10.1275

A deep learning method for the automatic modulation recognition of received radio signals  

Kim, Hanjin (Department of Computer Engineering, Chungnam National University)
Kim, Hyeockjin (Department of Computer Engineering, Chungnam National University)
Je, Junho (Department of Computer Engineering, Chungnam National University)
Kim, Kyungsup (Department of Computer Engineering, Chungnam National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.10, 2019 , pp. 1275-1281 More about this Journal
Abstract
The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.
Keywords
Automatic modulation classification(AMC); Deep learning; Long-short term memory(LSTM); Neural network; Wireless communication;
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