Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications  

作　　者：Zhang Mengjiao Liu Yu Huang Jie He Ruisi Zhang Jingfan Yu Chongyang Wang Chengxiang 

机构地区：[1]School of Microelectronics,Shandong University,Jinan 250101,China [2]The State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China [3]The National Mobile Communications Research Laboratory,School of Information Science and Engineering,Southeast University,Nanjing 210096,China [4]The Purple Mountain Laboratories,Nanjing 211111,China

出　　处：《China Communications》2024年第3期260-272,共13页中国通信（英文版）

基　　金：supported by the National Key R&D Program of China under Grant 2021YFB1407001;the National Natural Science Foundation of China (NSFC) under Grants 62001269 and 61960206006;the State Key Laboratory of Rail Traffic Control and Safety (under Grants RCS2022K009);Beijing Jiaotong University, the Future Plan Program for Young Scholars of Shandong University;the EU H2020 RISE TESTBED2 project under Grant 872172

摘　　要：A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

关 键 词：artificial intelligence channel characteristic prediction HST channel millimeter wave scenario classification 

分 类 号：U285[交通运输工程—交通信息工程及控制] TP18[交通运输工程—道路与铁道工程] TN929.5[自动化与计算机技术—控制理论与控制工程]