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Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2022 Vol.23 No.9 P.1378-1389
Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
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.
Key words: Line-of-sight probability model; Air-to-ground channel; Machine learning; Ray tracing
1南京航空航天大学电子信息工程学院电磁频谱空间认知动态系统工信部重点实验室，中国南京市，211106
2西安电子科技大学综合业务网理论及关键技术国家重点实验室，中国西安市，710000
3南京航空航天大学电子信息工程学院雷达成像与微波光子技术教育部重点实验室，中国南京市，211106
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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DOI:
10.1631/FITEE.2200041
CLC number:
TN928
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On-line Access:
2022-09-21
Received:
2022-02-01
Revision Accepted:
2022-09-21
Crosschecked:
2022-07-15