Spectrogram-Based Automatic Modulation Recognition Using Convolutional Neural Network

• Published in: 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) pp. 843 - 845
• Main Authors: Jeong, Sinjin, Lee, Uhyeon, Kim, Suk Chan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2018
• Subjects: Automatic Modulation Recognition; Classification algorithms; Conferences; Convolutional Neural Network; Modulation; Neural networks; Short-Time Fourier Transform; Signal processing algorithms; Spectrogram
• ISSN: 2165-8536
• DOI: 10.1109/ICUFN.2018.8436654

We study a system for classifying modulation types with spectrograms obtained through short-time Fourier transform. AWGN-based carrier modulated signals and their spectrograms are generated. In order to extract features from spectrogram automatically, we learned our convolutional neural network model with the generated data. Even at low SNRs, the performance is fairly good, but additional modulation type applications and comparisons with others in various environments are necessary.