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基于深度神经网络接收器的水下声学正交频分复用通信：河流试验结果

Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results.

作者信息

Thenginthody Hassan Sabna, Chen Peng, Rong Yue, Chan Kit Yan

机构信息

School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University, Bentley, WA 6102, Australia.

出版信息

Sensors (Basel). 2024 Sep 15;24(18):5995. doi: 10.3390/s24185995.

DOI:10.3390/s24185995
PMID:39338740
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435698/
Abstract

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions.

摘要

本文提出了一种基于深度神经网络（DNN）的水下声学（UA）通信接收机。传统的正交频分复用（OFDM）接收机使用线性插值进行信道估计。然而，由于多径UA信道中存在显著的时延扩展，导频子载波之间的频率响应常常呈现出很强的非线性。由于信道延迟分布通常是未知的，这种非线性无法精确建模。基于神经网络（NN）的接收机通过神经网络训练学习并补偿这种非线性，有效应对了这一挑战。基于DNN的UA通信接收机的性能最近在西澳大利亚的河流试验中进行了测试。试验获得的结果证明，基于DNN的接收机比传统的基于最小二乘（LS）估计器的接收机性能更好。本文表明，使用DNN接收机的UA通信在革新水下通信系统方面具有巨大潜力，能够实现更高的数据速率、更高的可靠性，并增强对不断变化的水下条件的适应性。

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