基于机器学习的5G移动通信高铁道路场景无线信道建模  

Wireless Channel Modeling of 5G Mobile Communication High-speed Railway Scene Based on Machine Learning

作　　者：宾远 丁瑜 邓敏茜 程乔 卢盛辉 BIN Yuan;DING Yu;DENG Minqian;CHENG Qiao;LU Shenghui(China Mobile Communications Group Wuzhou branch,Wuzhou,Guangxi 543000,China;Nanning vocational and technical college,Nanning 530008,China;Guangxi construction vocational and technical college,Nanning 530007,China)

机构地区：[1]中国移动通信集团广西有限公司梧州分公司,广西梧州543000 [2]南宁职业技术大学,南宁530008 [3]广西建设职业技术学院,南宁530007

出　　处：《移动信息》2025年第3期8-9,15,共3页Mobile Information

基　　金：2024年度广西高校中青年教师科研基础能力提升项目:基于机器学习的5G移动通信高铁场景无线信道的建模研究(2024KY1062)。

摘　　要：高铁沿线移动通信系统无线信道模型的准确性对于无线网络质量有着重要影响,将机器学习方法引入无线移动通信信道建模领域具有重要实践意义。文中论述了高铁沿线道路覆盖场景特点和机器学习方法特点,提出了基于人工神经网络的信道建模方案,并用实测数据分析了其特点。随着对6G移动通信系统的不断探索,未来将尝试采用机器学习方法对6G移动通信无线信道进行建模。The accuracy of wireless channel models for mobile communication systems along high-speed railways has a significant impact on wireless network quality,and introducing machine learning methods into the field of wireless mobile communication channel modeling has important practical significance.This paper discusses the characteristics of road coverage scenarios and machine learning methods along high-speed rail lines,proposes a channel modeling scheme based on artificial neural networks,and analyzes its characteristics using measured data.With the continuous exploration of 6G mobile communication systems,future attempts will be made to use machine learning methods to model the wireless channels of 6G mobile communication.

关 键 词：移动通信 机器学习 信道建模 

分 类 号：TN929[电子电信—通信与信息系统]