基于混合神经网络的紫外光散射信道盲均衡方法  

Blind Equalization Method for Ultraviolet Light Scattering Channel Based onHybrid Neural Network

作　　者：赵太飞[1,2] 孙玉歆 潘飞翔 张爽 Zhao Taifei;Sun Yuxin;Pan Feixiang;Zhang Shuang(Faculty of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,Shaanxi,China;Xi'an Key Laboratory of Wireless Optical Communication and Network Research,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 [2]西安市无线光通信与网络研究重点实验室,陕西西安710048

出　　处：《光学学报》2024年第18期224-233,共10页Acta Optica Sinica

基　　金：国家自然科学基金(61971345);陕西省重点研发计划(2021GY-044);人工智能四川省重点实验室开发基金(2022RYY01);西安市科技计划(23GXFW0060)。

摘　　要：针对非直视无线紫外光通信受大气散射等多种因素影响而存在严重的脉冲展宽和信号衰减的问题,本文提出了一种基于混合神经网络的紫外光散射信道盲均衡方法。该方法将长短期记忆递归神经网络(LSTM)和深度神经网络(DNN)相结合,把接收信号视为时间序列,不依赖先验的信道知识,充分利用LSTM强大的时间记忆序列学习能力提取接收信号特征,从而恢复原始信号。仿真结果显示,信噪比大于11 dB时,本文算法的误码率相较最小均方算法和递归最小二乘算法可降低1~2个数量级,均方误差可降低0.5个数量级以上;与单一的DNN相比,本文算法的均衡性能也有显著提升。信噪比等于11 dB时,其误码率和均方误差分别降低了81.0%和27.8%,证明提出的混合神经网络具有更强的噪声抑制能力。Objective Wireless ultraviolet scattering communication is a wireless communication technology based on atmospheric particle scattering.Due to its strong scattering characteristics,wireless ultraviolet can be applied to special scenarios such as non-direct vision.However,this strong scattering effect can lead to an obvious multipath effect of wireless ultraviolet and cause serious pulse broadening.In the case of a high data rate,this phenomenon will cause inter-symbol interference and even cause information misjudgment,leading to the increase of bit error rate and poorer communication performance.To improve wireless ultraviolet communication,it is necessary to study the signal processing technology for ultraviolet scattering channel.As a key technology in wireless optical communication,channel equalization can effectively suppress or eliminate inter-symbol interference.As an artificial intelligence method,deep learning has developed rapidly in recent years.With wide application,it can also be applied to the signal processing of wireless optical communication,which inspires channel equalization.In this paper,we combine deep learning technology with ultraviolet optical communication to achieve more efficient and intelligent wireless ultraviolet optical communication.Methods We study the channel problem of wireless ultraviolet(UV)scattering communication,and establish a single scattering channel model for non-line-of-sight UV.We analyze the scattering channel characteristics in terms of impulse response and path loss,to provide a suitable channel model for subsequent equalization.Then,we combine long short term memory recurrent neural network(LSTM)and deep neural network(DNN)to develop a blind equalization method for UV scattering channel based on a hybrid neural network,which can preprocess the training data into a time sequence,and process the temporal dependence of the input signals through LSTM to extract useful temporal features.The nonlinear features of the signal data are further explored using DNN to enhance

关 键 词：无线紫外光信道 单次散射 神经网络 盲均衡 脉冲展宽 

分 类 号：TN929.12[电子电信—通信与信息系统]