End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution

To address the problems of the high complexity and poor bit error rate (BER) performance of traditional communication systems in underwater acoustic environments, this paper incorporates the theory of deep learning into a conventional communication system and proposes data-driven underwater acoustic filter bank multicarrier (FBMC) communications based on convolutional autoencoder networks. The proposed system is globally optimized by two one-dimensional convolutional (Conv1D) modules at the transmitter and receiver, it realizes signal reconstruction through end-to-end training, it effectively avoids the inherent imaginary interference of the system, and it improves the reliability of the communication system. Furthermore, dense-block modules are constructed between Conv1D layers and are connected across layers to achieve feature reuse in the network. Simulation results show that the BER performance of the proposed method outperforms that of the conventional FBMC system with channel equalization algorithms such as least squares (LS) estimation and virtual time reversal mirrors (VTRM) under the measured channel conditions at a certain moment in the Qingjiang River.

Automated summary

This paper proposes a data-driven underwater acoustic filter bank multicarrier (FBMC) communication system based on convolutional autoencoder networks, which incorporates deep learning theory into traditional communication systems to address the problems of high complexity and poor bit error rate (BER) performance in underwater acoustic environments. The proposed system achieves global optimization through two one-dimensional convolutional (Conv1D) modules at the transmitter and receiver, realizes signal reconstruction through end-to-end training, effectively avoids inherent interference, and improves the reliability of the communication system. Furthermore, dense-block modules are introduced for feature reuse in the network. Simulation results demonstrate that the proposed method outperforms conventional FBMC systems with channel equalization algorithms under specific measured channel conditions in the Qingjiang River at a certain moment.