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Toward the Automatic Modulation Classification With Adaptive Wavelet Network
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DOI：10.1109/TCCN.2023.3252580.png)
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摘要
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With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment. Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully connected layers for classification. However, existing schemes still are insufficient in utilizing feature maps. To overcome this limitation, a novel adaptive wavelet network (AWN) is proposed, which combines adaptive wavelet decomposition based on the lifting scheme and channel attention mechanism.In contrast to the previous models, the multi-level decomposition of AWN explicitly extracts the features of multiple frequency bands. The channel attention mechanism further selects the optimal frequencies from the candidate frequencies. AWN explores a novel AMC paradigm that efficiently integrates the inherent properties of the signal by introducing prior knowledge in the frequency domain. Simulation results demonstrate that our proposed AMC scheme outperforms the benchmark scheme and has rather low computational complexity.
Keyword:
Wavelet transforms
Feature extraction
Modulation
Deep learning
Convolutional neural networks
Convolution
Frequency modulation
Automatic modulation classification
deep learning
adaptive wavelet
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被引数:
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