Deep Learning Based Single copyright Communications Over Time-Varying Underwater Acoustic Channel

Deep Learning Based Single copyright Communications Over Time-Varying Underwater Acoustic Channel

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In recent years, deep learning (DL) techniques have shown great potential in wireless communications.Unlike DL-based receivers for time-invariant or slow time-varying channels, we propose a new DL-based receiver sara stedy stand aid for single copyright communication in time-varying underwater acoustic (UWA) channels.Without the off-line training, the proposed receiver alternately works with online training and test modes for accommodating the time variability of UWA channels.

Simulation results show a better detection performance achieved by the proposed DL-based receiver and with a considerable reduction in training overhead compared to the traditional channel-estimate (CE)-based decision feedback equalizer (DFE) in simulation scenarios with a measured sound speed profile.The proposed receiver has also been tested by using the data recorded in an experiment in the South China Sea at a communication range of 8 km.The performance of the receiver is evaluated for various training overheads and noise levels.

Experimental results demonstrate that the beer button down shirts for men proposed DL-based receiver can achieve error-free transmission for all 288 burst packets with lower training overhead compared to the traditional receiver with a CE-based DFE.