Research Article
GPP-SDR Based GSM-R Air Interface Monitoring System and Its Big Data Interference Analysis
@INPROCEEDINGS{10.1007/978-3-030-06161-6_69, author={Xiang Chen and Zhongfa Li}, title={GPP-SDR Based GSM-R Air Interface Monitoring System and Its Big Data Interference Analysis}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={GSM-R monitoring GPP-SDR framework Big data analysis C/S structure}, doi={10.1007/978-3-030-06161-6_69} }- Xiang Chen
Zhongfa Li
Year: 2019
GPP-SDR Based GSM-R Air Interface Monitoring System and Its Big Data Interference Analysis
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_69
Abstract
In the railway transportation industry, the monitoring of Global System for Mobile Communications for Railway (GSM-R) network is essential, which plays an important role in safety of the train. The traditional monitoring systems are mainly based on the A/Abis or PRI interfaces. Therefore, the traditional ways are difficult to monitor random interferences and faults occurred over wireless channels, which may causes the potential security menace. In this paper, we propose a GSM-R monitoring system based on the Um interface. Adopting the General Purpose Processor (GPP)-Software Defined Radio (SDR) framework, the GSM-R network can be monitored by full Um interface information, including spectrum, signaling and traffic information. We use the GPP-SDR based front-end processors to obtain the data from Um interface, which are transmitted to the center servers in a railway bureau data center. After receiving the original data, the C/S structure based center servers will process the data for users to monitor. The whole system design has been implemented and deployed in Guangzhou Railway Bureau, including Guang-Shen Line and Guang-Shen-Gang Line. Furthermore, a big data interference analysis framework is proposed based on the Um interface monitoring database, which has also been verified to successfully capture and classify traditional types of interferences in field tests.
