A general empirical path loss(PL) model for air-to-ground(A2 G) millimeter-wave(mm Wave) channels is proposed in this paper. Different from existing PL models, the new model takes the height factor of unmanned aerial ...A general empirical path loss(PL) model for air-to-ground(A2 G) millimeter-wave(mm Wave) channels is proposed in this paper. Different from existing PL models, the new model takes the height factor of unmanned aerial vehicles(UAVs) into account, and divides the propagation conditions into three cases(i.e., line-of-sight, reflection,and diffraction). A map-based deterministic PL prediction algorithm based on the ray-tracing(RT) technique is developed, and is used to generate numerous PL data for different cases. By fitting and analyzing the PL data under different scenarios and UAV heights, altitude-dependent model parameters are provided. Simulation results show that the proposed model can be effectively used to predict PL values for both low-and high-altitude cases.The prediction results of the proposed model better match the RT-based calculation results than those of the Third Generation Partnership Project(3 GPP) model and the close-in model. The standard deviation of the PL is also much smaller. Moreover, the new model is flexible and can be extended to other A2 G scenarios(not included in this paper) by adjusting the parameters according to the simulation or measurement data.展开更多
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 scenar...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.展开更多
基金Project supported by the National Key Scientific Instrument and Equipment Development Project,China (No. 61827801)the Aeronautical Science Foundation of China (No. 201901052001)+2 种基金the Fundamental Research Funds for the Central Universities,China (Nos. NS2020026 and NS2020063)the State Key Laboratory of Integrated Services Network Funding,China (No. ISN22-11)the Open Foundation for Graduate Innovation of Nanjing University of Aeronautics and Astronautics (NUAA),China(No. KFJJ20200416)。
文摘A general empirical path loss(PL) model for air-to-ground(A2 G) millimeter-wave(mm Wave) channels is proposed in this paper. Different from existing PL models, the new model takes the height factor of unmanned aerial vehicles(UAVs) into account, and divides the propagation conditions into three cases(i.e., line-of-sight, reflection,and diffraction). A map-based deterministic PL prediction algorithm based on the ray-tracing(RT) technique is developed, and is used to generate numerous PL data for different cases. By fitting and analyzing the PL data under different scenarios and UAV heights, altitude-dependent model parameters are provided. Simulation results show that the proposed model can be effectively used to predict PL values for both low-and high-altitude cases.The prediction results of the proposed model better match the RT-based calculation results than those of the Third Generation Partnership Project(3 GPP) model and the close-in model. The standard deviation of the PL is also much smaller. Moreover, the new model is flexible and can be extended to other A2 G scenarios(not included in this paper) by adjusting the parameters according to the simulation or measurement data.
基金Project supported by the National Key Scientific Instrument and Equipment Development Project,China(No.61827801)the Open Research Fund of the State Key Laboratory of Integrated Services Networks,China(No.ISN22-11)。
文摘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.
