Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique–Deep Belief Network (DBN) – to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10 ⁻³ Bit Error Rate (BER).

中文翻译：

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使用深信度网络的水声通信接收器

水下环境为通信创造了一个具有挑战性的渠道。在本文中，我们通过探索机器学习技术——深度置信网络 (DBN)——来设计一种新型接收器系统，以对抗多普勒效应和多径传播引起的信号失真。我们在模拟实验和海试中评估了所提出的接收器系统的性能。我们提出的接收器系统包括基于 DBN 的接收信号去噪和分类。首先，在使用新颖的像素化算法对这些帧中的每一个进行单独预处理之前，将接收到的信号分割成帧。然后，使用基于 DBN 的去噪算法，从这些帧中提取特征并用于重建接收信号。最后，发生基于 DBN 的重构信号分类。 ⁻³误码率 (BER)。