5G城市轨道交通场景分类及信道建模

何睿斯; 艾渤; 钟章队; 杨汨; 黄晨; 马张枫; 孙桂琪; 米航; 周承毅; 陈瑞凤

doi:10.11959/j.issn.1000-0801.2021243

您当前的位置：

首页 >

文章列表页 >

5G城市轨道交通场景分类及信道建模

研究与开发 | 更新时间：2024-06-05

- 5G城市轨道交通场景分类及信道建模
- 5G urban rail traffic scenario classification and channel modeling
- 电信科学 2021年37卷第10期页码：102-116
- 作者机构：
  
  1. 北京交通大学轨道交通控制与安全国家重点实验室，北京 100044
  2. 中国铁道科学研究院集团有限公司电子计算技术研究所，北京 100081
- 作者简介：
  
  [ "何睿斯（1987− ），男，博士，北京交通大学轨道交通控制与安全国家重点实验室教授，主要研究方向为无线传播信道、铁路通信、5G和6G通信" ]
  [ "艾渤（1974− ），男，博士，北京交通大学轨道交通控制与安全国家重点实验室教授、常务副主任，主要研究方向为无线电传播、信道建模的研究和应用、铁路系统的全球移动通信系统、铁路系统的长期发展" ]
  [ "钟章队（1962− ），男，北京交通大学轨道交通控制与安全国家重点实验室教授，主要研究方向为铁路无线通信、铁路控制理论和技术、GSM-R系统" ]
  [ "杨汨（1992− ），男，博士，北京交通大学轨道交通控制与安全国家重点实验室副教授，主要研究方向为信道建模、车对车通信、智能交通系统" ]
  [ "黄晨（1991− ），男，北京交通大学博士生，主要研究方向为通道参数分析和表征、用于时变信道建模的聚类和跟踪、基于机器学习的传播信道特征描述技术的应用" ]
  [ "马张枫（1991− ），男，北京交通大学博士生，主要研究方向为无线电传播模型、无人机通信、无线信道建模" ]
  [ "孙桂琪（1993− ），女，北京交通大学博士生，主要研究方向为RIS无线信道建模" ]
  [ "米航（1995− ），男，北京交通大学博士生，主要研究方向为毫米波信道建模和可重构智能表面辅助无线通信" ]
  [ "周承毅（1993− ），男，北京交通大学硕士生，主要研究方向为高速铁路通信覆盖扩展技术" ]
  [ "陈瑞凤（1989− ），女，博士，中国铁道科学研究院电子计算技术研究所副研究员，主要研究方向为智能铁路通信和铁路传感器网络" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2020YFB1806903);国家自然科学基金资助项目;The National Natural Science Foundation of China(61922012);国家自然科学基金资助项目;The National Natural Science Foundation of China(62001519)
- DOI：10.11959/j.issn.1000-0801.2021243
  中图分类号： TN92
- 网络出版日期：2021-10，
  
  纸质出版日期：2021-10-20
- 稿件说明：
移动端阅览
何睿斯, 艾渤, 钟章队, 等. 5G城市轨道交通场景分类及信道建模[J]. 电信科学, 2021,37(10):102-116.

Ruisi HE, Bo AI, Zhangdui ZHONG, et al. 5G urban rail traffic scenario classification and channel modeling[J]. Telecommunications science, 2021, 37(10): 102-116.
何睿斯, 艾渤, 钟章队, 等. 5G城市轨道交通场景分类及信道建模[J]. 电信科学, 2021,37(10):102-116. DOI： 10.11959/j.issn.1000-0801.2021243.

Ruisi HE, Bo AI, Zhangdui ZHONG, et al. 5G urban rail traffic scenario classification and channel modeling[J]. Telecommunications science, 2021, 37(10): 102-116. DOI： 10.11959/j.issn.1000-0801.2021243.

摘要

城市轨道交通是现代化交通基础设施的重要组成部分，5G作为新一代移动通信技术，可提供高速率、低时延的无线数据传输，有助于提升城市轨道交通的运行效率和服务质量。由于城市轨道交通场景的复杂性，需要针对性的通信场景分类、信道特性分析和精准的信道模型为城市轨道交通5G通信系统的设计提供理论支撑。基于此，提出了5G城市轨道交通电波传播场景的分类，以支撑典型场景下的信道测试与建模工作，同时阐述了城市轨道交通场景信道测量和建模的现状，并分析了当前面临的主要挑战。结合5G通信智能化特点，讨论了人工智能在信道特征提取和信道建模方面的应用前景与可行思路，并深入分析了基于可重构智能面和无人飞行器辅助的5G城市轨道交通信道建模研究现状和发展前景。最后，阐述了毫米波频段下5G城市轨道交通信道建模的研究。

Abstract

Urban rail traffic is an important part of modern transportation infrastructure.As a new generation of mobile communication technology

5G can provide high data rate and low latency wireless transmission

which helps to improve the efficiency and service quality of urban rail traffic system.Due to the complexity of urban rail traffic scenarios

accurate communication scenario classification

channel characterization and channel models are required to provide theoretical support for the design of urban rail traffic 5G communication systems.The classification of 5G urban rail traffic radio propagation scenarios to support channel measurements and modeling was proposed.Current status of urban rail traffic channel measurements and modeling was shown

and the current challenges were analyzed.The applications of artificial intelligence in channel feature extraction and channel modeling were discussed

and the 5G urban rail traffic channels by considering reconfigurable intelligent surface and unmanned aerial vehicle were analyzed.Finally

the research on 5G urban rail traffic channel modeling at millimeter wave frequency band was described.

关键词

Keywords

references

AI B , CHENG X , KÜRNER T , , et al . Challenges toward wireless communications for high-speed railway [J ] . IEEE Transactions on Intelligent Transportation Systems , 2014 , 15 ( 5 ): 2143 - 2158 .

AI B , HE R S , ZHONG Z D , et al . Radio wave propagation scene partitioning for high-speed rails [J ] . International Journal of Antennas and Propagation , 2012 : 1 - 7 .

WANG C X , BIAN J , SUN J , et al . A survey of 5G channel measurements and models [J ] . IEEE Communications Surveys＆ Tutorials , 2018 , 20 ( 4 ): 3142 - 3168 .

WANG C X , GHAZAL A , AI B , et al . Channel measurements and models for high-speed train communication systems:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2016 , 18 ( 2 ): 974 - 987 .

ZHANG J H , TANG P , YU L , et al . Channel measurements and models for 6G:current status and future outlook [J ] . Frontiers of Information Technology ＆ Electronic Engineering , 2020 , 21 ( 1 ): 39 - 61 .

ZHANG B , ZHONG Z D , HE R S , et al . Measurement-based Markov modeling for multi-link channels in railway communication systems [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 3 ): 985 - 999 .

HE R S , ZHONG Z D , AI B , et al . Measurements and analysis of propagation channels in high-speed railway viaducts [J ] . IEEE Transactions on Wireless Communications , 2013 , 12 ( 2 ): 794 - 805 .

ZHANG B , ZHONG Z D , HE R S , et al . Measurement-based multiple-scattering model of small-scale fading in high-speed railway cutting scenarios [J ] . IEEE Antennas and Wireless Propagation Letters , 2017 , 16 : 1427 - 1430 .

LI J Z , AI B , HE R S , et al . Cluster-based 3D channel modeling for massive MIMO in subway station environment [J ] . IEEE Access , 2018 , 6 : 6257 - 6272 .

AI B , GUAN K , ZHONG Z D , et al . Measurement and analysis of extra propagation loss of tunnel curve [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 4 ): 1847 - 1858 .

WANG J , LI R , WANG J , et al . Artificial intelligence and wireless communications [J ] . Frontiers of Information Technology ＆Electronic Engineering , 2020 , 21 ( 10 ): 1413 - 1425 .

LIU Y , WANG C X , HUANG J . Recent developments and future challenges in channel measurements and models for 5G and beyond high-speed train communication systems [J ] . IEEE Communications Magazine , 2019 , 57 ( 9 ): 50 - 56 .

AI B , MOLISCH A F , RUPP M , et al . 5G key technologies for smart railways [J ] . Proceedings of the IEEE , 2020 , 108 ( 6 ): 856 - 893 .

HE R S , AI B , MOLISCH A F , et al . Clustering enabled wireless channel modeling using big data algorithms [J ] . IEEE Communications Magazine , 2018 , 56 ( 5 ): 177 - 183 .

HUANG C , MOLISCH A F , GENG Y L , et al . Trajectory-joint clustering algorithm for time-varying channel modeling [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 1 ): 1041 - 1045 .

HUANG C , HE R S , ZHONG Z D , et al . A power-angle-spectrum based clustering and tracking algorithm for time-varying radio channels [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 1 ): 291 - 305 .

HUANG C , MOLISCH A F , HE R S , et al . Machine-learning-based data processing techniques for vehicle-to-vehicle channel modeling [J ] . IEEE Communications Magazine , 2019 , 57 ( 11 ): 109 - 115 .

KURNIAWAN E , ZHIWEI L , SUN S M . Machine learning-based channel classification and its application to IEEE 802.11ad communications [C ] // Proceedings of GLOBECOM 2017 - 2017 IEEE Global Communications Conference . Piscataway:IEEE Press , 2017 : 1 - 6 .

XIAO Z L , WEN H K , MARKHAM A , et al . Non-line-of-sight identification and mitigation using received signal strength [J ] . IEEE Transactions on Wireless Communications , 2015 , 14 ( 3 ): 1689 - 1702 .

VAN NGUYEN T , JEONG Y , SHIN H , et al . Machine learning for wideband localization [J ] . IEEE Journal on Selected Areas in Communications , 2015 , 33 ( 7 ): 1357 - 1380 .

HUANG C , MOLISCH A F , HE R S , et al . Machine learning-enabled LOS/NLOS identification for MIMO systems in dynamic environments [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 6 ): 3643 - 3657 .

YANG M , AI B , HE R S , et al . Machine-learning-based scenario identification using channel characteristics in intelligent vehicular communications [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 7 ): 3961 - 3974 .

WU Q Q , ZHANG R . Towards smart and reconfigurable environment:intelligent reflecting surface aided wireless network [J ] . IEEE Communications Magazine , 2020 , 58 ( 1 ): 106 - 112 .

LONG R Z , LIANG Y C , PEI Y Y , et al . Active reconfigurable intelligent surface-aided wireless communications [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 8 ): 4962 - 4975 .

JUNG M , SAAD W , DEBBAH M , et al . On the optimality of reconfigurable intelligent surfaces (RISs):passive beamforming,modulation,and resource allocation [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 7 ): 4347 - 4363 .

SHOJAEIFARD A , WONG K K , DI RENZO M , et al . Massive MIMO-enabled full-duplex cellular networks [J ] . IEEE Transactions on Communications , 2017 , 65 ( 11 ): 4734 - 4750 .

LIASKOS C , NIE S , TSIOLIARIDOU A , et al . A new wireless communication paradigm through software-controlled metasurfaces [J ] . IEEE Communications Magazine , 2018 , 56 ( 9 ): 162 - 169 .

YANG B , CAO X L , HUANG C W , et al . Intelligent spectrum learning for wireless networks with reconfigurable intelligent surfaces [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 4 ): 3920 - 3925 .

HUANG C W , ZAPPONE A , ALEXANDROPOULOS G C , et al . Reconfigurable intelligent surfaces for energy efficiency in wireless communication [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 8 ): 4157 - 4170 .

TAHA A , ZHANG Y , MISMAR F B , et al . Deep reinforcement learning for intelligent reflecting surfaces:towards standalone operation [C ] // Proceedings of 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) . Piscataway:IEEE Press , 2020 : 1 - 5 .

TANG W K , CHEN M Z , CHEN X Y , et al . Wireless communications with reconfigurable intelligent surface:path loss modeling and experimental measurement [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 1 ): 421 - 439 .

BASAR E , YILDIRIM I , KILINC F . Indoor and outdoor physical channel modeling and efficient positioning for reconfigurable intelligent surfaces in mmWave bands [J ] . IEEE Transactions on Communications , 3954 , PP ( 99 ): 1 .

HE R S , SCHNEIDER C , AI B , et al . Propagation channels of 5G millimeter-wave vehicle-to-vehicle communications:recent advances and future challenges [J ] . IEEE Vehicular Technology Magazine , 2020 , 15 ( 1 ): 16 - 26 .

DI RENZO M , ZAPPONE A , DEBBAH M , et al . Smart radio environments empowered by reconfigurable intelligent surfaces:how it works,state of research,and the road ahead [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 11 ): 2450 - 2525 .

RENZO M D , DEBBAH M , PHAN-HUY D T , , et al . Smart radio environments empowered by reconfigurable AI meta-surfaces:an idea whose time has come [J ] . EURASIP Journal on Wireless Communications and Networking,2019 , 2019 :129.

YOU C S , ZHENG B X , ZHANG R . Channel estimation and passive beamforming for intelligent reflecting surface:discrete phase shift and progressive refinement [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 11 ): 2604 - 2620 .

ZHENG B X , ZHANG R . Intelligent reflecting surface-enhanced OFDM:channel estimation and reflection optimization [J ] . IEEE Wireless Communications Letters , 2020 , 9 ( 4 ): 518 - 522 .

MATTHIESEN B , BJÖRNSON E , DE CARVALHO E , et al . Intelligent reflecting surface operation under predictable receiver mobility:a continuous time propagation model [J ] . IEEE Wireless Communications Letters , 2021 , 10 ( 2 ): 216 - 220 .

KHAWAJA W , GUVENC I , MATOLAK D W , et al . A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles [J ] . IEEE Communications Surveys ＆ Tutorials , 2019 , 21 ( 3 ): 2361 - 2391 .

KHUWAJA A A , CHEN Y F , ZHAO N , et al . A survey of channel modeling for UAV communications [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 4 ): 2804 - 2821 .

CHENG X , LI Y R , WANG C X , et al . A 3-D geometry-based stochastic model for unmanned aerial vehicle MIMO ricean fading channels [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 9 ): 8674 - 8687 .

JIA H L , CHEN H , WANG S W , et al . 3D non-stationary unmanned aerial vehicles＆apos; MIMO channel model [J ] . IET Communications , 2019 , 13 ( 18 ): 2941 - 2945 .

JIANG H , ZHANG Z C , GUI G . Three-dimensional non-stationary wideband geometry-based UAV channel model for A2G communication environments [J ] . IEEE Access , 2019 , 7 : 26116 - 26122 .

JIANG H , ZHANG Z C , WU L , et al . Three-dimensional geometry-based UAV-MIMO channel modeling for A2G communication environments [J ] . IEEE Communications Letters , 2018 , 22 ( 7 ): 1438 - 1441 .

JIA R B , LI Y R , CHENG X , et al . 3D geometry-based UAV-MIMO channel modeling and simulation [J ] . China Communications , 2018 , 15 ( 12 ): 64 - 74 .

MA Z F , AI B , HE R S , et al . A wideband non-stationary air-to-air channel model for UAV communications [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 2 ): 1214 - 1226 .

MA Z F , AI B , HE R S , et al . Impact of UAV rotation on MIMO channel characterization for air-to-ground communication systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 11 ): 12418 - 12431 .

CUI Y P , FANG X M , YAN L . Hybrid spatial modulation beamforming for mmWave railway communication systems [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 12 ): 9597 - 9606 .

HEMADEH I A , SATYANARAYANA K , EL-HAJJAR M , , et al . Millimeter-wave communications:physical channel models,design considerations,antenna constructions,and link-budget [J ] . IEEE Communications Surveys ＆ Tutorials , 2017 , 20 ( 2 ): 870 - 913 .

PFADLER A , BALLESTEROS C , ROMEU J , et al . Hybrid massive MIMO for urban V2I:sub-6 GHz vs mmWave performance assessment [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 5 ): 4652 - 4662 .

YAN L , FANG X M , FANG Y G . Stable beamforming with low overhead for C/U-plane decoupled HSR wireless networks [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 7 ): 6075 - 6086 .

TARIQ M H , CHONDROULIS I , SKARTSILAS P , et al . MmWave massive MIMO channel measurements for fixed wireless and smart city applications [C ] // Proceedings of 2020 IEEE 31st Annual International Symposium on Personal,Indoor and Mobile Radio Communications . Piscataway:IEEE Press , 2020 : 1 - 6 .

HUANG J , WANG C X , FENG R , et al . Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems [J ] . IEEE Journal on Selected Areas in Communications , 2017 , 35 ( 7 ): 1591 - 1605 .

MORENO GARCIA-LOYGORRI J , BRISO C , ARNEDO I , et al . Intra-train propagation between 26-40 GHz for 5G applications [C ] // Proceedings of 12th European Conference on Antennas and Propagation (EuCAP 2018) . Institution of Engineering and Technology , 2018 .

NONAKA N , MURAOKA K , OKUYAMA T , et al . 28 GHz-band experimental trial at 283 km/h using the shinkansen for 5G evolution [C ] // Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) . Piscataway:IEEE Press , 2020 : 1 - 5 .

HE R S , AI B , WANG G P , et al . High-speed railway communications:from GSM-R to LTE-R [J ] . IEEE Vehicular Technology Magazine , 2016 , 11 ( 3 ): 49 - 58 .

LIU Y , WANG C X , LOPEZ C F , et al . 3D non-stationary wideband tunnel channel models for 5G high-speed train wireless communications [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 1 ): 259 - 272 .

CHANG Y Y , FURUKAWA M , SUZUKI H , et al . Propagation analysis with ray tracing method for high speed trains environment at 60 GHz [C ] // Proceedings of 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) . Piscataway:IEEE Press , 2015 : 1 - 5 .

YANG M , AI B , HE R S , et al . Machine-learning-based fast angle-of-arrival recognition for vehicular communications [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 2 ): 1592 - 1605 .

浏览量

588

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

5G信道建模与性能测试方法

基于生成对抗网络的超宽带数字信道建模

面向垂直行业专网的确定性SLA指标分级研究

5G多路冗余传输技术综述

星地融合下的手机直连关键技术研究