无人机空中基站对地信道建模及功率覆盖预测
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1.南京航空航天大学电磁频谱空间认知动态系统工业和信息化部重点实验室,南京,211106;2.南京航空航天大学计算机科学与技术学院,南京,211106

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国家重大科学仪器设备开发专项 61827801┫资助项目 ; 国家自然科学基金重点 61631020┫资助项目 ; 江苏省物联网与控制技术重点实验室基金 NJ20160027┫资助项目 国家重大科学仪器设备开发专项(61827801)资助项目;国家自然科学基金重点(61631020)资助项目;江苏省物联网与控制技术重点实验室基金(NJ20160027)资助项目。


Channel Modeling and Coverage Prediction for Link between UAV-Based Base Stations and Ground
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1.Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China;2.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

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    摘要:

    传统统计建模方法难以对特定场景下的无人机信道做出精确预测。针对城市场景下的无人机信道,结合确定性信道建模法,基于射线跟踪原理构建了一种无人机基站-地信道模型,该模型考虑了散射次数对接收功率的影响,将到达地面接收机的射线分为视距路径、一次散射路径和二次散射路径;给出了基于射线跟踪原理的传播损耗和3种路径相应的功率计算方法,该方法沿路径计算场强。在利用数字地图预处理技术对城市建筑物进行简化后,对该场景下无人机信道的传播路径、损耗、功率延迟分布以及功率覆盖情况进行了仿真验证。数值仿真结果表明,本模型能够准确复现城市传播环境及传播状况,覆盖预测结果可用于优化无人机部署。

    Abstract:

    Traditional statistical methods do not provide accurate prediction for UAV channels under specific scenarios. For the urban scenarios, a geometry-based stochastic channel model for the communication link between UAV-based base stations and ground is developed by a deterministic method based on the ray tracing principle. The proposed model considers the influence of scattering times on received power, and divides arrival rays to the ground into three types, e.g., the line-of-sight component, the single-bounced component, and the double-bounced component. By calculating the electric field along the propagation path and using the ray tracing principle, the computation methods for the path loss and received power of three kinds of components are given. After the digital map is pre-processed, the propagation paths, path loss, power delay profile, and power coverage are simulated and verified. Numerical and analytical results show that the proposed model can accurately reconstruct the propagation situation and the coverage predictions can be used on the UAV layout optimization.

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杨婧文,陈小敏,仲伟志,朱秋明,陈兵,姚梦恬.无人机空中基站对地信道建模及功率覆盖预测[J].数据采集与处理,2019,34(6):1125-1132

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  • 收稿日期:2019-01-09
  • 最后修改日期:2019-03-22
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  • 在线发布日期: 2019-12-13