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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. [ABSTRACT FROM AUTHOR]

By contraposing the signal detection for filter bank multicarrier (FBMC) communications with the underwater acoustic (UWA) channel, this paper analyzes the traditional imaginary interference problem and proposes a deep learning-based method. The neural network with feature extraction and automatic learning ability is employed to replace the demodulation modules to recover transmitted signals without explicit channel estimation and equalization. Sufficient data sets are generated according to the measured channel conditions in Qingjiang river, the optimization of network parameters is finished by constraining cost function in offline training, and the signal detection is carried out directly with the well-trained network in online testing. The system performance of various supervised learning models such as multilayer perceptron (MLP), convolutional neural network (CNN), and bidirectional long short-term memory (BLSTM) network is compared under different data sizes, network parameters, and prototype filters. The simulation results show that the bit error rate (BER) performance of the proposed signal detection is better than that of the classic one, which indicates that deep learning is a promising tool in UWA communication systems. [ABSTRACT FROM AUTHOR]