机器学习的时变环境下自由空间光通信系统信道建模研究  

Research on channel modeling of free space optical communication systems in time-varying environments based on machine learning

作　　者：周书兴 唐露新 ZHOU Shuxing;TANG Luxin(Guangzhou Institute of Science and Technology,Guangzhou 510540,China;Guangdong Engineering Research Center for Industrial Robot Integration and Application,Guangzhou Institute of Science and Technology,Guangzhou 510540,China)

机构地区：[1]广州理工学院,广州510540 [2]广州理工学院广东省工业机器人集成与应用工程技术研究中心,广州510540

出　　处：《激光杂志》2024年第4期172-176,共5页Laser Journal

基　　金：2022年广东省教育科学规划课题(No.2022GXJK373)。

摘　　要：在时变环境下,传统模型只去除了部分通信串扰,传输损耗大,为此提出机器学习的时变环境下自由空间光通信系统信道建模方法。采用直方图统计法统计数据集的峰值、选择通信最短路径,遗传算法得到信道最佳参数,机器学习根据最佳参数计算通信阻抗,获得电容和电导值,对权值进行调整去除通信串扰,根据信道规模特性将天线域转换为波束域,搭建时变环境下自由空间光通信信道。仿真实验结果表明,本模型的传输损耗平均达到148 dB,具有较高的应用价值。In time-varying environments,traditional models only remove some communication crosstalk and have high transmission losses.Therefore,a machine learning channel modeling method for free space optical communication systems in time-varying environments is proposed.The histogram statistical method is used to calculate the peak values of the dataset,select the shortest communication path,obtain the optimal channel parameters through genetic algo-rithm,calculate the communication impedance based on the optimal parameters through machine learning,obtain ca-pacitance and conductivity values,adjust the weights to remove communication crosstalk,convert the antenna domain into beam domain based on the channel size characteristics,and build a free space optical communication channel in a time-varying environment.The simulation experimental results show that the average transmission loss of the model in this paper reaches 148 dB,which is relatively low and has high application value.

关 键 词：机器学习 时变环境 自由空间光通信 传输损耗 

分 类 号：TN927[电子电信—通信与信息系统]