深度学习辅助水下光通信信号检测算法仿真及实验研究  

Deep Learning Aided Signal Detection Algorithm Experimental Research for Underwater Optical Communication

作　　者：叶鹏飞 张鹏[2] 于浩[2] 何爽 田东生 王圆鑫 佟首峰[2] YE Pengfei;ZHANG Peng;YU Hao;HE Shuang;TIAN Dongsheng;WANG Yuanxin;TONG Shoufeng(School of Electronic Information Engineering,Changchun University of Science and Technology,Changchun 130012,China;School of Electro-optical Engineering,Changchun University of Science and Technology,Changchun 130012,China;Zhongshan Research Institute,Changchun University of Science and Technology,Zhongshan 528400,China;College of Communication Engineering,Jilin University,Changchun 130012,China)

机构地区：[1]长春理工大学电子信息工程学院,长春130022 [2]长春理工大学光电工程学院,长春130022 [3]长春理工大学中山研究院,中山528400 [4]吉林大学通信工程学院,长春130022

出　　处：《光子学报》2024年第7期235-245,共11页Acta Photonica Sinica

基　　金：国家自然基金重点项目(No.62231005);国家重点研发计划(No.2022YFB2903402);吉林省教育厅基金(Nos.JJKH20220746KJ,JJKH20220771KJ,JJKH20210820KJ)。

摘　　要：针对水下光通信提出了深度学习辅助的信号检测方法,设计并搭建了室内水下光通信实验平台,测试了所构建的三种水箱信道(水流、浑浊水流1、浑浊水流2)的数学模型,对所提方法进行了仿真测试,采集实验数据集对比研究了三种信道下所提方法与自适应阈值法的性能。针对不同水下信道、5 Mbps通信传输,三种信道下所提方法误比特率相比自适应阈值法最高分别降低了2个数量级、1个数量级、1个数量级。针对浑浊水流信道1、不同通信速率(5 Mbps、10 Mbps、25 Mbps)通信传输,三种速率下所提方法误比特率均降低了1个数量级。相比自适应阈值法,所提方法在复杂信道下水下无线光通信提高性能方面具有一定作用,可为高速可靠水下无线光通信系统设计提供一定参考。Underwater wireless optical communication has garnered significant attention in the wireless communication field due to its high data rate,enhanced security,and lightweight nature.However,seawater can induce absorption and scattering of light.Absorption results in a reduction of the received optical power at the receiver,which is an irreversible process,while scattering causes alterations in the received photons at the receiver.Moreover,the ocean typically contains turbulence,a phenomenon caused by temperature variations and irregular movements,leading to random fluctuations in the optical signal.Consequently,the underwater channel is intricate and challenging to predict.To achieve reliable communication performance,a more dependable signal detection method is required at the receiver.In this study,a deep learning-assisted signal detection method is proposed for underwater optical communication.A convolutional neural network(a specialized form of deep neural network)is developed to directly detect the Original On-off Keying(OOK)signal,and two distinct training methods for the Deep Neural Network(DNN)are proposed during the training phase.Initially,an indoor underwater optical communication experimental platform is designed and constructed,incorporating three types of water tank channels(flowing water,turbid flow 1,turbid flow 2).The attenuation coefficients and probability density functions of the channels are measured.Subsequently,a simulated underwater optical channel is derived based on the measured channel mathematical models,and a simulated dataset of OOK signals for the neural network is obtained.The proposed methods are tested using the dataset,and the performance of the two different DNN training methods and the adaptive threshold method is simulated under different simulated channels.The proposed methods exhibit an improvement in Bit Error Rate(BER)compared to the adaptive threshold method at any signal-to-noise ratio in the three channels.The improvement is most notable in the simplest flow channel,with

关 键 词：光通信 通断键控调制 深度学习 深度神经网络 信号检测 

分 类 号：TN929.1[电子电信—通信与信息系统]