基于被动时间反转-卷积神经网络的 OFDM水声通信系统研究

水声（Underwater Acoustic，UWA）信道的多径效应和多普勒效应造成正交频分复 用（Orthogonal Frequency Division Multiplexing，OFDM）水声通信系统接收端符号间干扰和载 波间干扰，降低系统性能 . 构造一种新型的被动时间反转-卷积神经网络（Passive Time Reversal-Convolutional Neural Network，PTR-CNN），并将其应用于 OFDM 水声通信系统接收 端 . PTR-CNN 网络的构造包括两部分，首先，基于被动时间反转理论削弱多径增强主路径信 息能量；其次，将上述输出结果转换成二维矩阵，再输入卷积神经网络中进行信号检测，同时 对抗多径和多普勒效应带来的干扰；最后，网络输出直接恢复比特流 . 仿真和试验结果表明， 与目前主流信道估计和信号检测算法相比，所提方法能够提升系统的可靠性，在不同水声信 道环境测试中均具有较好的鲁棒性.

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

The multipath effect and Doppler effect of the Underwater Acoustic (UWA) channel cause intersymbol 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 ReversalConvolutional Neural Network (PTR-CNN) is constructed and applied to the OFDM UWA communication system re? ceiver. The PTR-CNN network consists of two parts. Firstly, it weakens the multipath and enhances the main path in? formation 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 interfer? ence caused by the multipath and Doppler effect. Finally, the network output directly restores the bit stream. Simula? tion 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 robust? ness in different UWA channel environment tests.