智慧城轨交通通信技术的分析与展望

doi:10.11959/j.issn.1000-0801.2021029

Abstract

Abstract:

The particularity of the urban rail transit environment leads to the problems such as reducing communication reliability, delay performance and operating efficiency bottlenecks.Therefore, the demand for urban rail communication was analyzed from the perspective of overall operational efficiency.New intelligent communication control technologies such as T2T (train to train) communication, 5G, artificial intelligence, and mobile edge computing were combined to summarize and prospect the key technologies of urban rail communication.Then, a new type of urban rail communication network architecture was proposed.Finally, the research direction and challenges of smart urban rail communication technology were discussed, which also provided a research foundation for the development of smart cities.

Key words: urban rail transit, train control system, train to train communication, mobile edge computing, artificial intelligence

CLC Number:

TN929

Junhui ZHAO, Danyang ZHANG, Lin HE. Analysis and prospect of communication technology in smart urban rail[J]. Telecommunications Science, 2021, 37(4): 1-13.

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References 35

[1]	SONG H F , SCHNIEDER E . Availability and performance analysis of train-to-train data communication system[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,20(7): 2786-2795.
[2]	JAHANZEB F , SOLER J . Radio communication for communications-based train control (CBTC):a tutorial and survey[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(3): 1377-1402.
[3]	CHEN R , LONG W X , MAO G Q ,et al. Development trends of mobile communication systems for railways[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(4): 3131-3141.
[4]	郜春海, 包峰 . 全自动运行系统的兴起与未来之路[J]. 城市轨道交通, 2019(2): 18-19.
	GAO C H , BAO F . The rise and future of fully automatic operation system[J]. China Metros, 2019(2): 18-19.
[5]	宁滨, 郜春海, 李开成 ,等. 中国城市轨道交通全自动运行系统技术及应用[J]. 北京交通大学学报, 2019,43(1): 1-6.
	NING B , GAO C H , LI K C ,et al. Technology and application of fully automatic operation system for urban rail transit in China[J]. Journal of Beijing Jiaotong University, 2019,43(1): 1-6.
[6]	ZHU L , YAO D Y , ZHAO H L . Reliability analysis of nextgeneration CBTC data communication systems[J]. IEEE Transactions on Vehicular Technology, 2019,68(3): 2024-2034.
[7]	鲁玉龙, 沈海燕, 解亚龙 ,等. 边缘计算融合NB-IoT在高铁联调联试中的关键技术研究[J]. 中国设备工程, 2020(7): 175-177.
	LU Y L , SHEN H Y , XIE Y L ,et al. Research on the key technology of edge computing fusion NB-IoT in high-speed rail joint test[J]. China Plant Engineering, 2020(7): 175-177.
[8]	谢红霞, 孙林祥 . 轨道交通车-地通信无线局域网技术应用[J]. 铁道通信信号, 2020,56(2): 63-66.
	XIE H X , SUN L X . Application of WLAN technology for rail transit train-ground communication[J]. Railway Signalling ＆Communication, 2020,56(2): 63-66.
[9]	路向阳, 李东林, 李雷 ,等. 智慧城轨下智慧车辆装备技术的研究与展望[J]. 机车电传动, 2018(6): 1-8.
	LU X Y , LI D L , LI L ,et al. Study and prospect of equipment technology for smart rolling stock in smart urban rail transit system[J]. Electric Drive for Locomotives, 2018(6): 1-8.
[10]	MAO Q , HU F , HAO Q . Deep learning for intelligent wireless networks:a comprehensive survey[J]. IEEE Communications Surveys ＆ Tutorials, 2018,20(4): 2595-2621.
[11]	LIU X , XU Y H , CHENG Y P ,et al. A heterogeneous information fusion deep reinforcement learning for intelligent frequency selection of HF communication[J]. China Communications, 2018,15(9): 73-84.
[12]	CUI Y , MARTIN U , ZHAO W T . Calibration of disturbance parameters in railway operational simulation based on reinforcement learning[J]. Journal of Rail Transport Planning ＆Management, 2016,6(1): 1-12.
[13]	YANG Z P , ZHU F Q , LIN F . Deep-reinforcement-learning-based energy management strategy for supercapacitor energy storage systems in urban rail transit[J]. IEEE Transactions on Intelligent Transportation Systems, 2021,22(2): 1150-1160.
[14]	ZHAO J H , ZHANG Y , NIE Y W ,et al. Intelligent resource allocation for train-to-train communication:a multi-agent deep reinforcement learning approach[J]. IEEE Access, 2020(8): 8032-8040.
[15]	谭俊杰, 梁应敞 . 面向智能通信的深度强化学习方法[J]. 电子科技大学学报, 2020,49(2): 169-181.
	TAN J J , LIANG Y C . Deep reinforcement learning for intelligent communications[J]. Journal of University of Electronic Science and Technology of China, 2020,49(2): 169-181.
[16]	WANG X X , LIU L J , ZHU L ,et al. Train-centric CBTC meets age of information in train-to-train communications[J]. IEEE Transactions on Intelligent Transportation Systems, 2020,21(10): 4072-4085.
[17]	WANG X X , LIU L J , TANG T ,et al. Enhancing communication-based train control systems through train-to-train communications[J]. Transactions on Intelligent Transportation Systems, 2019,20(4): 1544-1561.
[18]	PASCOE R D , EICHORN T N . What is communication-based train control[J]. IEEE Vehicular Technology Magazine, 2009,4(4): 16-21.
[19]	WANG H W , YU F R , JIANG H L . Modeling of radio channels with leaky coaxial cable for LTE-M based CBTC systems[J]. IEEE Communications Letters, 2016,20(5): 1038-1041.
[20]	ZHAO J H , LIU J , NI S J ,et al. Enhancing transmission on hybrid precoding based train-to-train communication[J]. Mobile Networks and Applications, 2020,25(5): 2082-2091.
[21]	倪善金, 赵军辉 . 5G无线通信网络物理层关键技术[J]. 电信科学, 2015,31(12): 48-53.
	NI S J , ZHAO J H . Key technologies in physical layer of 5G wireless communications network[J]. Telecommunications Science, 2015,31(12): 48-53.
[22]	ANDREAS L , STRANG T ， RICO-GARCíA C . A reliable surveillance strategy for an autonomous rail collision avoidance system[C]// Proceedings of the 15th ITS World Congress. New York:ACM Press, 2008.
[23]	何芊颖 . 基于TSSM的车车通信系统车载移动授权模块的建模和验证[D]. 北京:北京交通大学, 2017.
	HE Q Y . TSSM-based modeling and verification of VBTC MA unit in VOBC[D]. Beijing:Beijing Jiaotong University, 2017.
[24]	徐纪康 . 基于车－车通信的新型CBTC系统分析[J]. 铁道通信信号, 2014,50(6): 78-80.
	XU J K . Analysis of a new CBTC system based on train-train communication[J]. Railway Signalling ＆ Communication, 2014,50(6): 78-80.
[25]	林俊亭, 王晓明, 党垚 ,等. 城市轨道交通列车碰撞防护系统设计与研究[J]. 铁道科学与工程学报, 2015,12(2): 407-413.
	LIN J T , WANG X M , DANG Y ,et al. Design and research of the improved train control system with collision avoidance system for urban mass transit[J]. Journal of Railway Science and Engineering, 2015,12(2): 407-413.
[26]	郜城城, 周旭, 范鹏飞 ,等. 移动边缘计算技术在高铁通信网络中的应用[J]. 计算机系统应用, 2018,27(8): 56-62.
	GAO C C , ZHOU X , FAN P F ,et al. Application of mobile edge computing technology in high speed rail communication network[J]. Computer Systems ＆ Applications, 2018,27(8): 56-62.
[27]	李斌 . 基于 MEC 的高速铁路无线通信网络优化方案[J]. 电信科学, 2019,35(11): 88-95.
	LI B . Wireless communication network optimization scheme for high-speed railway based on MEC[J]. Telecommunications Science, 2019,35(11): 88-95.
[28]	HALTUF M . Shift rail JU from member state’s point of view[J]. Transportation Research Procedia, 2016(14): 1819-1828.
[29]	ZHANG J Y , BJ?RNSON E , MATTHAIOU M ,et al. Prospective multiple antenna technologies for beyond 5G[J]. IEEE Journal on Selected Areas in Communications, 2020,38(8): 1637-1660.
[30]	YAN L , FANG X M , WANG X B ,et al. AI-enabled sub-6-GHz and mm-wave hybrid communications:considerations for use with future HSR wireless systems[J]. IEEE Vehicular Technology Magazine, 2020,15(3): 59-67.
[31]	刘云毅, 赵军辉, 王传云 . 高速铁路宽带无线通信系统越区切换技术[J]. 电信科学, 2017,33(11): 37-46.
	LIU Y Y , ZHAO J H , WANG C Y . Handover technology in high-speed railway broadband wireless communication system[J]. Telecommunications Science, 2017,33(11): 37-46.
[32]	艾渤, 章嘉懿, 何睿斯 ,等. 面向智能高铁业务和应用的 5G基础理论与关键技术[J]. 中国科学基金, 2020,34(2): 133-141.
	AI B , ZHANG J Y , HE R S ,et al. Fundamental theory and key technologies of 5G for service and application of intelligent high-speed railway[J]. Bulletin of National Natural Science Foundation of China, 2020,34(2): 133-141.
[33]	SOLIMAN M , SIEMONS J , KOCHEMS J ,et al. Automatic train coupling:challenges and key enablers[J]. IEEE Communications Magazine, 2019,57(9): 32-38.
[34]	ZHAO J H , NI S J , YANG L H ,et al. Multiband cooperation for 5G HetNets:a promising network paradigm[J]. IEEE Vehicular Technology Magazine, 2019,14(4): 85-93.
[35]	MORENO J , RIERA J M , HARO L D ,et al. A survey on future railway radio communications services:challenges and opportunities[J]. IEEE Communications Magazine, 2015,53(10): 62-68.

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参数	取值
带宽B	10 MHz
BS覆盖半径	3 km
天线增益	7 dBi
BS接收机噪声系数	5 dB
列车的天线增益	4 dBi
列车接收机噪声系数	8 dB
列车速度	65～90 km/h
相邻列车间的距离	600～900 m
T2W发射功率	43 dBm
T2T发射功率等级	{10,23,33} dBm
T2W链路路损模型	37.6 lg d + 128.1 dB
T2T链路路损模型	40 lg d + 148 dB
学习率α	0.001
折扣率γ	0.4
权重因子λ	{0.1,0.15,0.18,0.2,0.205,0.25}

通信解决方案	系统架构	通信模式	信号系统数据吞吐	理论最高支持列车运行速度	优势与限制
TBTC	轨道电路	单向传输车到地信息	/	80 km/h	无须无线传输，但基于电气化轨道电路的安全隐患高
WLAN	轨旁 AP+无线传输媒介	车地间双向实时传输	3 Mbit/s	140 km/h	支持无线传输，但数据速率低，车地传输设备复杂，极易受到干扰
LTE-M	轨旁BBU+RRU+无线传输媒介	车地间双向实时传输	10 Mbit/s	200 km/h	提供更高的数据传输速率，但频谱资源稀缺，频谱利用率较低
LTE-M+MEC	同上并增加MEC服务器	车地间双向实时传输	≥10 Mbit/s	200 km/h	满足较高数据传输速率并优化传输时延，但成本较高
T2T	在 CBTC 系统基础上增加车车通信设备，简化车地通信	车地间双向实时传输，并增加冗余车到车通信	WLAN：≥3 Mbit/s LTE-M:≥10 Mbit/s	WLAN：140 km/h LTE-M:200 km/h	提升列车安全性能以及运行效率，但频谱资源限制导致新的资源分配问题
5G-M	基于5G通信系统	车地间双向实时传输	≥500 Mbit/s	250 km/h	数据传输速率极高，但覆盖范围小，存在频繁切换以及高昂的建设成本问题

Analysis and prospect of communication technology in smart urban rail

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