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面向电力应用场景的5G信道测量与路损模型修正

5G Channel Measurement and Pass Loss Model Correction for Power Application Scenario
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摘要 传统公网5G信道模型一般针对城市、街区等场景进行建模,在变电站等电力工业场景中适应性不足。为支撑5G在电网的规模化应用,需要开展对特定电力场景的信道测量与建模以指导电力5G无线网络规划和系统级仿真。本文在500 kV变电站开展5G频段的实地信道测量,分析了时延功率谱、路径损耗等信道特性,并在此基础上提出了一种引入天线高度矫正因子的路损模型修正方法,以便更准确地对天线高度与路损之间的关系进行建模,同时对比分析与5G标准路损模型之间的差异。结果表明,该模型能更好地适应电力应用场景,可为通信性能评估提供参考与指导,并为电力通信设备的覆盖范围预测提供理论依据。 The traditional public network 5G channel model is generally modeled for scenarios such as cities and blocks,and has insufficient adaptability in power industry scenarios such as substations.In order to support the large-scale application of 5G in the power grid,it is necessary to carry out channel measurement and modeling for specific power scenarios to guide power 5G wireless network planning and system-level simulation.In this paper,field channel measurement of 5G frequency band was carried out in 500kV substation,and channel characteristics such as delay power spectrum and path loss were analyzed.On this basis,a path loss model correction method with antenna height correction factor was proposed to model the relationship between antenna height and path loss more accurately.At the same time,the difference between the standard road loss model and 5G is compared and analyzed.The results show that the model can better adapt to the electric power application scenario,provide reference and guidance for the evaluation of communication performance,and provide a theoretical basis for the coverage prediction of electric power communication equipment.
作者 王海洋 熊稀南 苏俊浩 黄正宇 刘明刚 刘丹谱 WANG Hai-yang;XIONG Xi-nan;SU Jun-hao;HUANG Zheng-yu;LIU Ming-gang;LIU Dan-pu(Shandong Electric Power Engineering Consulting Institute Co.,Ltd.,Jinan 250100,China;School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处 《中国电子科学研究院学报》 北大核心 2023年第9期808-813,820,共7页 Journal of China Academy of Electronics and Information Technology
基金 国家自然科学基金资助项目(61971069)。
关键词 电力5G场景 信道测量 路损模型 矫正因子 power 5g scenario channel measurement pass loss model corrective factor
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