水下无线光深度自动编码器通信性能  

Communication Performance of Underwater Wireless Optical Deep Autoencoder

作　　者：陈丹[1] 王睿[1] 艾菲尔 汤林海 Chen Dan;Wang Rui;Ai Feier;Tang Linhai(School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,Shaanxi,China)

机构地区：[1]西安理工大学自动化与信息工程学院,陕西西安710048

出　　处：《光学学报》2024年第12期1-10,共10页Acta Optica Sinica

基　　金：国家自然科学基金(62371390);陕西省重点研发计划(2023-YBGY-039,2020GY-036);西安市高校院所人才服务企业项目(GXYD14.21);西安市无线光通信与网络研究重点实验室项目。

摘　　要：自动编码器利用深度神经网络联合优化发射机和接收机实现端到端通信。与具有模块化结构的传统通信不同,基于深度学习的通信系统可根据不同编码方案学习最优映射空间。海洋环境中吸收、散射以及湍流效应严重影响水下无线光通信系统性能,基于考虑Gamma-Gamma湍流和传输路径损耗影响的水下联合信道模型,针对自动编码器独热矢量数据传输速率有限的问题,提出一种水下自动编码器自适应传输方案,在不同海洋信道以及不同网络训练条件下根据均方误差性能约束选择最优传输矢量,实现传输速率最大化并提高通信性能。仿真结果表明自动编码器与传统通信相比可获得更优的误码率(BER)性能,且不同训练参数集合下自适应传输方案均可获得比传统独热矢量更低的BER与更高的数据速率。Objective Underwater wireless optical communication(UWOC)has a longer transmission distance and a higher data rate compared with underwater radio frequency communication and underwater acoustic communication.However,the absorption,scattering,and turbulence effects in the marine environment seriously affect the transmission quality of the optical signals,resulting in a limited transmission rate and an increased bit error rate(BER)of the UWOC system.Autoencoders can achieve end-to-end UWOC performance by using deep neural networks to jointly optimize the transmitter and receiver.However,as one of the most important data representation methods in autoencoders,the one-hot vector has a low data transmission rate.In order to solve these issues,in this paper,we propose an adaptive transmission scheme for underwater autoencoders based on deep neural networks on a joint channel that considers Gamma-Gamma turbulence and transmission path loss.This scheme can effectively suppress the impacts of underwater turbulence,absorption,and scattering on the performance of UWOC systems,improve the data rate of underwater autoencoders,and reduce the BER of the system.Methods In this paper,an adaptive transmission scheme for underwater autoencoders with mean square error(MSE)performance constraints was proposed by using the deep neural network.The UWOC channel model was established by using the path loss of the Beer-Lambert law and the probability density function of the Gamma-Gamma underwater turbulence distribution.By simulating the performance of the autoencoder’s non-adaptive one-hot vector and comparing it with that of the adaptive transmission scheme under different UWOC channel conditions,the effects of different turbulence intensities,received signal-to-noise ratios(SNRs),and training parameter ensembles on the non-adaptive and adaptive transmission performance of the underwater autoencoder were discussed,respectively.Results and Discussions In this paper,an adaptive transmission scheme for underwater autoencoders is proposed

关 键 词：水下无线光通信 自动编码器 自适应传输 深度学习 误码率 

分 类 号：TN929.3[电子电信—通信与信息系统]