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Training Images Generation for CNN Based Automatic Modulation Classification
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DOI：10.1109/ACCESS.2021.3073845.png)
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摘要
En 中文
Convolutional neural network (CNN) models have recently demonstrated impressive classification and recognition performance on image and video processing scope. In this paper, we investigate the application of CNN to identifying modulation classes for digitally modulated signals. First, the received baseband data samples of modulated signal are gathered up and transformed to generate the constellation-like training images for convolutional networks. Among the resulting training images, the proposed convolutional gray image is preferred for network training and inference because of the lower computational burden. Second, we propose to use a multiple-scale convolutional neural network (MSCNN) as the classifier. The skip-connection technique is deployed for mitigating the negative effect of vanishing gradients and overfitting during the network training process. Numerical simulations have been carried out to validate the effectiveness of the proposed scheme, the results show that the proposed scheme outperforms the traditional algorithms in terms of classification accuracy.
Keyword:
Modulation
Computational modeling
Training
Feature extraction
Constellation diagram
Load modeling
Convolutional neural networks
Convolutional neural network
automatic modulation classification
deep learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
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IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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