智能反射面辅助的5G高铁车站覆盖增强技术研究

Intelligent reflecting surface-assisted 5G high-speed railway station coverage enhancement technology

为助力铁路数字化转型,基于5G的铁路专用移动通信(5G for railway, 5G-R)系统成为铁路智能联接的首选.本文利用前沿技术赋能铁路新一代移动通信,开展了智能反射面(intelligent reflecting surface,IRS)辅助5G高铁车站覆盖增强的研究.采用射线跟踪技术,精准刻画了2.1 GHz频段下高铁车站场景的电波传播特性;基于准确的电波传播特性,利用反射面、发射机和接收机三者之间的角度关系,对IRS部署进行了研究,并设计了相应的IRS波束指向;在获得IRS辅助下的信道传递函数后,对部署IRS前后的路径损耗(path loss,PL)和阴影衰落进行了建模和比较.结果表明:IRS的部署为目标区域带来了最大8.1 dB、平均4.63 dB的信号增益;目标区域的PL指数由未部署时的2.68减小至2.33,阴影衰落标准差由9.45 dB减小至6.43 dB.因此,部署IRS能够显著提高室外宏站对车站内部的信号覆盖,缓解车站建筑物遮挡对5G信号传输的影响,为车站场景下5G-R系统的设计与优化提供了理论基础和数据支撑.

To facilitate the digital transformation of railways, the 5G for railway(5G-R) system will become the first choice for railways intelligent connections. This paper employs advanced technologies to promote the new generation railway mobile communication, and conducts a research on intelligent reflecting surface(IRS) assisted 5G high-speed railway station coverage enhancement. In this paper, the ray tracing technology is exploited to accurately model the radio propagation characteristics of the high-speed railway station scenario at the 2.1 GHz frequency band.Based on the accurate radio propagation characteristics, the angle relationship among the reflector, transmitter and receiver, the IRS deployment strategies are proposed, and the corresponding IRS beam direction is designed. After obtaining the channel transfer function influenced by IRS, the path loss(PL) model and the shadow fading model are compared in the case of with IRS and without IRS. The outcomes indicate that the IRS deployment delivers a maximum signal gain of 8.1 dB and an average of 4.63 dB to the target area. The PL index reduce from 2.68 to 2.33, and the standard deviation of shadow fading reduce from 9.45 dB to 6.43 dB. Consequently, the deployment of IRS can significantly improve the radio coverage of the outdoor base station, alleviate the impact of the obstruct of the station buildings on the 5G signal transmission. The research can provide theoretical basis and data support for the design and optimization of the 5G-R system in the station scenario.