Digital Modulation Recognition Based on Wavelet Denoising and Convolution Neural Network
Article No.: 40, Pages 1 - 6
Abstract
It is difficult for traditional modulation recognition methods to extract feature parameters manually and their anti-noise ability is weak. The existing deep learning methods have high computational complexity. This paper proposes a digital modulation recognition algorithm based on wavelet denoising and convolution neural network (WD-CNN). In this paper, the signal is firstly processed by wavelet threshold denoising to reduce the interference of noise. Then the signal is mapped to the constellation diagram, and the constellation diagram is input into convolution neural network to extract features after normalizing. At last the modulation mode of signal is identified by the Softmax classifier and the Gradient-weighted Class Activation Mapping (Grad-CAM) is used to explain the performance of the convolution neural network. We use MATLAB R2019b software to generate data sets under different signal-to-noise ratio (SNR) conditions and then teste the sets. The results show that this method has high accuracy of modulation recognition, strong anti-noise ability, low time complexity and low memory consumption.
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Index Terms
- Digital Modulation Recognition Based on Wavelet Denoising and Convolution Neural Network
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Published In
December 2020
366 pages
ISBN:9781450389532
DOI:10.1145/3448823
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Association for Computing Machinery
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Published: 04 March 2021
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ICVISP 2020
ICVISP 2020: 2020 4th International Conference on Vision, Image and Signal Processing
December 9 - 11, 2020
Bangkok, Thailand
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ICVISP 2020 Paper Acceptance Rate 60 of 147 submissions, 41%;
Overall Acceptance Rate 186 of 424 submissions, 44%
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