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CLC number: TN928

On-line Access: 2022-09-21

Received: 2022-02-01

Revision Accepted: 2022-09-21

Crosschecked: 2022-07-15

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Citations:  Bibtex RefMan EndNote GB/T7714


Qiuming Zhu


Minghui PANG


Zhipeng LIN


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.9 P.1378-1389


Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications

Author(s):  Minghui PANG, Qiuming ZHU, Zhipeng LIN, Fei BAI, Yue TIAN, Zhuo LI, Xiaomin CHEN

Affiliation(s):  Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; more

Corresponding email(s):   pangminghui@nuaa.edu.cn, zhuqiuming@nuaa.edu.cn, linlzp@nuaa.edu.cn, baifei@nuaa.edu.cn, tian_yue@nuaa.edu.cn, lizhuo@nuaa.edu.cn, chenxm402@nuaa.edu.cn

Key Words:  Line-of-sight probability model, Air-to-ground channel, Machine learning, Ray tracing

Minghui PANG, Qiuming ZHU, Zhipeng LIN, Fei BAI, Yue TIAN, Zhuo LI, Xiaomin CHEN. Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(9): 1378-1389.

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author="Minghui PANG, Qiuming ZHU, Zhipeng LIN, Fei BAI, Yue TIAN, Zhuo LI, Xiaomin CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
%A Minghui PANG
%A Qiuming ZHU
%A Zhipeng LIN
%A Fei BAI
%A Zhuo LI
%A Xiaomin CHEN
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 9
%P 1378-1389
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200041

T1 - Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
A1 - Minghui PANG
A1 - Qiuming ZHU
A1 - Zhipeng LIN
A1 - Fei BAI
A1 - Yue TIAN
A1 - Zhuo LI
A1 - Xiaomin CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 9
SP - 1378
EP - 1389
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200041

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K-nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.


摘要:视距(line-of-sight, LoS)概率预测对于无线通信系统的性能优化至关重要。然而,由于无人机等飞行器飞行高度从十几米到数千米不等,空地(air-to-ground, A2G)通信的LoS概率预测具有挑战性。本文针对A2G场景,提出一种高度相关的经验性LoS概率模型。在模型参数估计之前,设计了一种基于K近邻(K-nearest neighbors, KNN)的策略对LoS和非视距(none-line-of-sight, NLoS)路径进行分类。然后,开发了一种基于双层反向传播神经网络(back propagation neural network, BPNN)的参数估计方法来建立每个模型参数与无人机高度之间的关系。仿真表明该模型获得的结果与射线追踪(ray trancing, RT)数据、实测数据和标准模型结果具有良好一致性。该模型还可提供比其他LoS概率模型更广泛的适用高度,能够应用于各种A2G场景下的不同通信高度。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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