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.
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.
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Minghui PANG and Qiuming ZHU designed the research. Zhipeng LIN and Fei BAI processed the data. Minghui PANG drafted the paper. Qiuming ZHU and Zhipeng LIN helped organize the paper. Yue TIAN helped train the data. Zhuo LI and Xiaomin CHEN revised and finalized the paper.
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Minghui PANG, Qiuming ZHU, Zhipeng LIN, Fei BAI, Yue TIAN, Zhuo LI, and Xiaomin CHEN declare that they have no conflict of interest.
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Project supported by the National Key Scientific Instrument and Equipment Development Project, China (No. 61827801) and the Open Research Fund of the State Key Laboratory of Integrated Services Networks, China (No. ISN22-11)
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Table S1 Trained parameters of the suburban scenario
Table S2 Trained parameters of the urban scenario
Table S3 Trained parameters of the dense urban scenario
Table S4 Trained parameters of the high-rise urban scenario
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Pang, M., Zhu, Q., Lin, Z. et al. Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications. Front Inform Technol Electron Eng 23, 1378–1389 (2022). https://doi.org/10.1631/FITEE.2200041
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DOI: https://doi.org/10.1631/FITEE.2200041