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ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification
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DOI：10.1109/LCOMM.2022.3179003.png)
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摘要
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Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdependence, we propose a novel AMC framework, where the autoencoder (AE) serves as the backbone and Convolution-AE and LSTM-AE are combined in a parallel way as temporal and spatial feature extractors. The comparisons with serval algorithms on the radioML2016.10a show that our proposed network can achieve higher classification accuracy at low SNR with a low cost. In addition, it suits the semi-supervised scenario since the dependence on labels is loosen.
Keyword:
Feature extraction
Modulation
Logic gates
Symbols
Kernel
Training
Image reconstruction
Automatic modulation classification
autoencoder
convolution
LSTM
semi-supervised
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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引用论文
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MCNet: An Efficient CNN Architecture for Robust Automatic Modulation ClassificationMCNet: 一种用于鲁棒自动调制分类的高效CNN架构
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