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基于机器学习的空地通信高度相关视距概率经验性模型
Enhanced PublicationMachine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
- 信息与电子工程前沿（英文） 2022年23卷第9期 页码：1378-1389
- Affiliations：
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, China
2.State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710000, China
3.Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
- Author bio：
E-mail: pangminghui@nuaa.edu.cn;
‡Corresponding authors
baifei@nuaa.edu.cn;
tian_yue@nuaa.edu.cn;
lizhuo@nuaa.edu.cn;
chenxm402@nuaa.edu.cn
- Funds：National Key Scientific Instrument and Equipment Development Project, China(61827801);Open Research Fund of the State Key Laboratory of Integrated Services Networks, China(ISN22-11)
DOI：10.1631/FITEE.2200041
中图分类号： TN928纸质出版日期：2022-09，
收稿日期：2022-02-01，
录用日期：2022-07-15
- Accepted：
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庞明慧, 朱秋明, 林志鹏, 等. 基于机器学习的空地通信高度相关视距概率经验性模型[J]. 信息与电子工程前沿（英文）, 2022,23(9):1378-1389.
MINGHUI PANG, QIUMING ZHU, ZHIPENG LIN, et al. 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.
庞明慧, 朱秋明, 林志鹏, 等. 基于机器学习的空地通信高度相关视距概率经验性模型[J]. 信息与电子工程前沿（英文）, 2022,23(9):1378-1389. DOI： 10.1631/FITEE.2200041.
MINGHUI PANG, QIUMING ZHU, ZHIPENG LIN, et al. 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. DOI： 10.1631/FITEE.2200041.
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。
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
<math id="M1"><mi>K</mi></math>
-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.
关键词
视距概率模型空地信道机器学习射线跟踪
Keywords
Line-of-sight probability modelAir-to-ground channelMachine learningRay tracing
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