Abstract
A deep learning-based direct sequence spread spectrum (DSSS) underwater acoustic (UWA) communication system was proposed in this study to improve the performance of the conventional system under Doppler effects and low signal-to-noise ratio (SNR). The Temporal Convolutional Network (TCN) model was used as the receiver of the system. Simulation results showed that the TCN-based M-ary DSSS UWA communication system outperformed the conventional system under Doppler effects and complex shallow water acoustic channel. A transfertraining algorithm was used for fine-tuning of the model based on sea real-time data to achieve optimal performance. Sea experimental results showed that the fine-tuned TCN model improved the communication effect under Doppler background in the complex dynamic environment of shallow water.
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