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• W4384120402 abstract "This paper proposes a deep transfer learning (DTL)-based variable Doppler frequency-hopping binary frequency-shift keying underwater acoustic communication system. The system uses a convolutional neural network (CNN) as the demodulation module of the receiver. This approach directly demodulates the received signal without estimating the Doppler. The DTL first uses the simulated communication signal data to complete the CNN training. It then copies a part of the convolution layers from the pre-trained CNN to the target CNN. After randomly initializing the remaining layers for the target CNN, it is trained by the data samples from the specific communication scenarios. During the training process, the CNN learns the corresponding frequency from each symbol in the selected frequency-hopping group through the Mel-spectrograms. Simulation and experimental data processing results show that the performance of the proposed system is better than conventional systems, especially when the transmitter and receiver of the communication system are in variable speed motion in shallow water acoustic channels." @default.
• W4384120402 created "2023-07-14" @default.
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• W4384120402 date "2023-07-01" @default.
• W4384120402 modified "2023-10-16" @default.
• W4384120402 title "Deep transfer learning-based variable Doppler underwater acoustic communications" @default.
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• W4384120402 doi "https://doi.org/10.1121/10.0020147" @default.
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