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
周书兴 1唐露新1
作者信息
- 1. 广州理工学院,广州 510540;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
- 折叠
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.
Abstract
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/自由空间光通信/传输损耗Key words
machine learning/time-varying environment/free space optical communication/free space optical communication/transmission loss引用本文复制引用
基金项目
广东省教育科学规划课题(2022)(2022GXJK373)
出版年
2024
NETL
NSTL
万方数据