# End-to-End Underwater Acoustic Communication Based on Autoencoder with Dense Convolution
###### Abstract  
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. underwater acoustic communications; deep learning; convolutional autoencoder networks; FBMC 202312253.12.1253.  
is gradually emerging. In the physical layer of communication systems, researchers have attempted to combine original signal processing modules, such as channel coding [6; 7], modulation identification [8; 9], channel estimation [10; 11; 12] and signal detection [13; 14; 15], with deep neural network models, demonstrating the effectiveness and potential of future data-driven wireless communication systems. For example, [PERSON] et al. [16] proposed to replace the channel estimation and signal detection module at the receiver with a deep neural network to optimize the conventional receiver and improve the robustness of the system. [PERSON] et al. [17] proposed a deep-neural-network-based underwater OFDM receiver with only one neural network for the whole signal processing, and [PERSON] et al. [18] proposed to use the basic CNN model for signal detection at the FBMC receiver, both of which achieved better BER performance than that of conventional receivers. However, the above approach only designs the receiver side of the communication system, so only local optimization can be achieved. If the conventional transmitter module is also involved, the system can be further data-driven and globally optimized. Autoencoder networks are typical of self-supervised learning and consist of an encoder network that aims to extract the input signal features and compress the data and a decoder network that is responsible for recovering the original data to the maximum extent possible. [PERSON] et al. [19] built a complete communications system solely composed of neural networks with open-source deep learning software libraries, which only presents a 1 dB worse block error rate than that of a practical baseline system. [PERSON] et al. [20] interpreted the communications system as an autoencoder, where the traditional signal recovery process is considered an end-to-end reconstruction task that strives to jointly optimize transmitter and receiver components in a deep learning way. [PERSON] et al. [21] analyzed two sources of end-to-end learning performance improvement in wireless channel models. One is to reduce the dependence on pilot quantity without losing BER performance, and the other is to completely eliminate orthogonal pilots by learning the receiver together with expert information. In this paper, we apply deep learning theory to the physical layer and propose underwater acoustic filter banks multicarrier communication based on convolutional autoencoder networks. The encoder and decoder network of the convolutional autoencoder network is distributed at both the transmitter and receiver for global optimization, and the signals are restored through compression and reconstruction.