Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 8, Issue 3, Pages 929-932Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2019.2900247
Keywords
Modulation recognition; convolutional neural network; time-frequency analysis; noise reduction
Categories
Funding
- National Natural Science Foundation of China [61531009]
- Shenzhen Science and Technology Program [JCYJ20170817110410346]
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Recent convolutional neural networks (CNNs)-based image processing methods have proven that CNNs are good at extracting features of spatial data. In this letter, we present a CNN-based modulation recognition framework for the detection of radio signals in communication systems. Since the frequency variation with time is the most important distinction among radio signals with different modulation types, we transform 1-D radio signals into spectrogram images using the short-time discrete Fourier transform. Furthermore, we analyze statistical features of the radio signals and use a Gaussian filter to reduce noise. We compare the proposed CNN framework with two existing methods from literature in terms of recognition accuracy and computational complexity. The experiments show that the proposed CNN architecture with spectrogram images as signal representation achieves better recognition accuracy than existing deep learning-based methods.
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