Research on OFDM Underwater Acoustic Communication System Based on Passive Time Reversal-convolutional Neural Network

被引：0

作者：

Fu X. ^{[1
]}

Wang S. ^{[1
]}

Hu Y. ^{[1
]}

机构：

[1] School of Marine Science and Technology, Tianjin University, Tianjin

来源：

Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2022年 / 49卷 / 08期

基金：

中国国家自然科学基金;

关键词：

deep leaning; Orthogonal Frequency Division Multiplexing(OFDM); passive time reversal; signal detection; underwater acoustic communication;

D O I：

10.16339/j.cnki.hdxbzkb.2022234

中图分类号：

学科分类号：

摘要：

The multipath effect and Doppler effect of the Underwater Acoustic (UWA) channel cause inter-symbol interference and inter-carrier interference at the receiver of the orthogonal frequency division multiplexing (OFDM) communication system, which degrades the system performance. A novel Passive Time Reversal-Convolutional Neural Network (PTR-CNN) is constructed and applied to the OFDM UWA communication system receiver. The PTR-CNN network consists of two parts. Firstly, it weakens the multipath and enhances the main path information energy based on passive time reversal theory. Secondly, the above-mentioned output result is converted into a two-dimensional matrix, which is input into the CNN for signal detection to simultaneously combat the interference caused by the multipath and Doppler effect. Finally, the network output directly restores the bit stream. Simulation and experimental results demonstrate that when compared with the current mainstream channel estimation and signal detection algorithms, the proposed method can improve the reliability of the system, and it has better robustness in different UWA channel environment tests. © 2022 Hunan University. All rights reserved.

引用

页码：169 / 178

页数：9

共 19 条

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