基于高性能射线跟踪的高铁车站场景5G-R网络优化技术

5G-R network optimization technology for high-speed railway station scenes based on high-performance ray tracing

针对目前反复路测及人工调试方式难以实现准确高效的铁路无线网络优化要求的弊端,面向5G-Railway(5G-R)频段,在高铁车站这一典型通信场景下,通过三维场景建模和射线跟踪技术,以提升无线网络优化效率和精度为目标,研究了不同反射、散射阶数与接收功率的关系,提出了面向车站场景的反/散射阶数功率增长模型和基于散射体空间分布及多径能量特征的无线网络基站参数优化算法.研究结果表明:本文提出的模型能够支撑射线跟踪传播机理的选择,保障网络覆盖预测结果的准确性;优化算法将车站场景的网络达标覆盖率较优化前提升了8.92%,验证了算法的可行性.研究成果可为完善我国智能铁路技术提供参考.

Currently,network optimization mainly uses methods of repeated tests and manual adjustment.These methods are difficult to achieve accurate and efficient railway mobile radio network optimization.By using 3D scenario modeling and ray tracing technology,the different reflection and scattering orders in a typical railway station scenario at the 5G-Railway(5G-R) frequency band are investigated.In order to improve railway mobile radio network optimization efficiency and accuracy,a reflection/scattering order power growth model and an optimization algorithm of base station parameters for radio networks based on the spatial distribution of scatterers and multipath energy for the railway station scenario are proposed.The outcomes indicate that the model can support determining the ray tracing propagation mechanism,which will bring accurate radio network coverage results.Moreover,the optimization algorithm improves the network coverage rate of the railway station scenario by 8.92% compared with the original results.Therefore,the proposed optimization algorithmic feasibility is demonstrated.The research result can provide a reference for improving intelligent railway technology.