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CLC number: TN928
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-07-15
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https://orcid.org/0000-0002-4995-5970
Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
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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.
@article{title="Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications",
author="Minghui PANG, Qiuming ZHU, Zhipeng LIN, Fei BAI, Yue TIAN, Zhuo LI, Xiaomin CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="9",
pages="1378-1389",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200041"
}
%0 Journal Article
%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 Yue TIAN
%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
TY - JOUR
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
Abstract: 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.
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