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- W4385956616 abstract "In this paper, we design a Convolutional Neural Network (CNN) for Sub-Nyquist modulation recognition and compare the performance of Long Short-Term Memory (LSTM) network and Convolutional Long Short-term Deep Neural Network (CLDNN) respectively. Unlike conventional modulation recognition task operates with Nyquist sampled rate, the new network architectures for Sub-Nyquist modulation recognition are specifically designed with a certain number of neurons, layers, and other hyperparameters to effectively extract key features from Sub-Nyquist sampled signals and process larger volumes of data. The simulation results demonstrate that the CNN network has the best recognition accuracy of 98.01% on the GBsense dataset, followed by the CLDNN of 96.81% and LSTM of 87.51% respectively." @default.
- W4385956616 created "2023-08-18" @default.
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- W4385956616 date "2023-06-16" @default.
- W4385956616 modified "2023-10-17" @default.
- W4385956616 title "Deep Learning Based Sub-Nyquist Modulation Recognition" @default.
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- W4385956616 doi "https://doi.org/10.1145/3608251.3608264" @default.
- W4385956616 hasPublicationYear "2023" @default.
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