基于SVM智能预测的车地多网融合无线通信系统方案的研究与设计

白轩; 廖志斌; 付嵩

基于SVM智能预测的车地多网融合无线通信系统方案的研究与设计

中国铁道科学研究院集团有限公司通信信号研究所, 北京 100081

详细信息

作者简介:
白轩，助理研究员；廖志斌，研究实习员。

中图分类号: U285.2:TP39
出版历程
- 收稿日期: 2018-01-16
- 刊出日期: 2018-08-25

Train-wayside multi-network integration wireless communication based on SVM intelligent prediction

Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited,Beijing 100081, China

摘要

摘要: 为提高城市轨道交通车地无线通信质量，提出了一种车地多网融合无线通信系统方案，方案利用线路中共存的多种车地无线网络提供多条通信链路，共同保障车地数据的稳定传输。针对如何选择最优网络进行数据传输这一关键技术点，采用支持向量机（SVM）分类方法实现智能选择。最后，通过搭建车地多网融合无线通信系统的仿真环境，进行实验仿真。仿真显示：列车在当前车地无线网络信号质量下降时，能够智能检测并切换至质量更优的无线网络完成车地数据通信。基于SVM智能预测的车地多网融合无线通信系统方案能够提高车地无线通信质量，该方案具备有效性和可行性。
- 车地通信 /
- 多网融合 /
- 支持向量机 /
- 网络选择
Abstract: In order to improve the stability of the train-wayside wireless communication of urban rail transit, a trainwayside wireless communication scheme based on multi-network integration was proposed in this paper which used a variety of wireless networks to ensure data transmission. Selecting the optimal network for data transmission was a key technical point in this scheme. The paper proposed an optimization network selection algorithm based on support vector machine(SVM) to solve network selection problems by classification method. Then experimental simulations were used to prove the availability and reliability of SVM on optimal network selection. In this paper, the simulation environment of multi-network integrated train-wayside communication was established. the simulation results show that the train can intelligently detect and switch to a better quality wireless network to complete train-wayside data communication when the signal quality of the current wireless network was degraded. The train-wayside multi-network integration wireless communication system scheme based on SVM can improve the wireless communication quality of train-wayside communication. The scheme has the effectiveness and feasibility.
- train-wayside communication /
- multi-network integration /
- support vector machine(SVM) /
- network selection

HTML全文

参考文献(11)

[1]	Baum-Snow N, Kahn M E, Voith R. Effects of Urban Rail Transit Expansions: Evidence from Sixteen Cities, 1970-2000[with Comment][J]. Brookings-Wharton Papers on Urban Affairs, 2005, 2005(1):147-206.
[2]	陈维明, 邹劲柏, 高伟. 基于通信的列车控制系统无线通信干扰分析及抗干扰方案[J]. 城市轨道交通研究， 2014, 17（8）：36-39.
[3]	朱光文. 地铁信号系统中车– 地无线通信传输的抗干扰研究[J]. 铁道标准设计，2012（8）：112-116.
[4]	李春. 城市轨道交通基于通信的列车控制系统车地无线通信优化方案[J]. 城市轨道交通研究，2011，14（9）：103-107.
[5]	王晓轩, 唐涛, 蒋海林, 等. 基于TD-LTE 的城市轨道交通车地通信系统越区切换算法[J]. 中国铁道科学, 2016, 37（3）：109-115.
[6]	J.Hultell and M. Berg. Generalized roaming and access selection in multi-operator environments. In Proceeding of Radio Vetenskapoch Kommunikation, 2005（6): 14-16.
[7]	李军，宋梅, 宋俊德. 一种通用的Beyond 3G Multi-Radio 接入架构[J]. 武汉大学学报：理学版，2005（S2）：102-105.
[8]	危韧勇，耿永强. 基于支持向量机的机车轴承故障诊断方法[J]. 铁路计算机应用， 2007，16（9）：52-54.
[9]	Deng N, Tian Y, Zhang C. Support vector machines. Optimization based theory, algorithms, and extensions[M]. Crc Press,2012.
[10]	Tong S, Chang E Y. Support vector machine active learning for image retrieval[C]//Proceedings of the Ninth Acm International Conference on Multimedia, 2001: 107-118.
[11]	Yang D Y, Shu-Han L I. Research on structure of support vector machine kernel function[J]. Journal of Science of Teachers College & University, 2010(2): 23-26.