| 面向5G无人机通信场景的传播路径概率预测模型 |
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| 引用本文： | 庞明慧,台鑫,吕崇玉,朱秋明,华博宇,李茁.面向5G无人机通信场景的传播路径概率预测模型[J].电波科学学报,2023,38(1):54-62. |
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| 作者姓名： | 庞明慧 台鑫 吕崇玉 朱秋明 华博宇 李茁 |
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| 作者单位： | 1.南京航空航天大学 电磁频谱空间认知动态系统工业与信息化部重点实验室, 南京 211106 |
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| 基金项目： | 国家自然科学基金面上项目(62271250)；国家重大科研仪器研制项目(61827801)；江苏省自然科学基金(BK20211182)；未来网络科研基金(FNSRFP-2021-YB-04) |
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| 摘 要： | 面向5G无人机空地场景中快速变化的飞行高度和散射环境导致传播路径呈现动态生灭，路径概率预测对于描述动态生灭进而构建准确的空地信道模型至关重要.本文假设城市场景的建筑物位置服从泊松分布，综合考虑物理环境的随机几何信息以及菲涅尔传播区域等因素，建立了无人机对地视距（line-of-sight, LoS）路径概率的预测模型，该模型适用于不同飞行高度和通信频率.在此基础上，利用反射路径镜像的原理，推导获得了地面反射（ground specular, GS）路径概率的预测模型.仿真和分析结果表明，本文提出的LoS和GS路径概率模型在不同高度和频点与基于海量射线追踪仿真方法的平均预测结果表现出良好的一致性.此外，LoS路径概率模型在低空场景下的预测结果与现有标准化模型结果也吻合.
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| 关 键 词： | 空地信道 路径概率 统计模型 菲涅尔区 射线跟踪 |
| 收稿时间： | 2022-05-28 |
Path probability prediction model for 5G UAV communication scenarios |
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| Affiliation: | 1.Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China2.Ceyear Technologies Co. Ltd., Qingdao 266555, China3.The Key Laboratory of Radar Imaging and Microwave Photonics, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China |
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| Abstract: | In the 5G air-to-ground (A2G) communication scenarios, the variable altitude and the changing scattering environment of unmanned aerial vehicles (UAVs) lead to the dynamic birth and death of the UAV communication propagation path. Therefore, path probability prediction is essential to describe the dynamic birth and death of propagation path and build the accurate A2G channel models. Considering the geometric information of the 3D scattering environment and the Fresnel zone, a line-of-sight (LoS) probability model is proposed in this paper based on the Poisson-distributed A2G urban scenarios, which is related to altitude and frequency. On this basis, we propose a ground specular (GS) path probability model for the first time in this paper using the method of mirroring the incident path. Simulation results show that the LoS and GS path probability models in this paper show good agreement with the average probability of massive ray tracing (RT) simulations at different communication altitudes and frequencies. Furthermore, the proposed LoS probability model is compatible with the existing standard models. |
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