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Channel Modeling for Air-to-Ground Wireless Communication
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作者 Yingcheng Shi Di He +1 位作者 Bin Li Jianwu Dou 《ZTE Communications》 2015年第2期41-45,共5页
In this paper, we discuss several large-scale fading models for different enviromnents. The COST231-Hata model is adapted for air-to-ground modeling. We propose two criteria for air-to- ground channel modelling based ... In this paper, we discuss several large-scale fading models for different enviromnents. The COST231-Hata model is adapted for air-to-ground modeling. We propose two criteria for air-to- ground channel modelling based on test data derived from field testing in Beijing. We develop a new propagation model that is more suitable for air-to-ground communication that previous models. We focus on improving this propagation model using the field test data. 展开更多
关键词 air-to-ground communication large-scale fading model
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Fixed-time adaptive model reference sliding mode control for an air-to-ground missile 被引量:8
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作者 Liang ZHANG Changzhu WEI +1 位作者 Rong WU Naigang CUI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第5期1268-1280,共13页
This paper addresses the fixed-time adaptive model reference sliding mode control for an air-to-ground missile associated with large speed ranges, mismatched disturbances and un-modeled dynamics. Firstly, a sliding mo... This paper addresses the fixed-time adaptive model reference sliding mode control for an air-to-ground missile associated with large speed ranges, mismatched disturbances and un-modeled dynamics. Firstly, a sliding mode surface is developed by the tracking error of the state equation and the model reference state equation with respect to the air-to-ground missile. More specifically,a novel fixed-time adaptive reaching law is presented. Subsequently, the mismatched disturbances and the un-modeled dynamics are treated as the model errors of the state equation. These model errors are estimated by means of a fixed-time disturbance observer, and they are also utilized to compensate the proposed controller. Therefore, the fixed-time controller is obtained by an adaptive reaching law and a fixed-time disturbance observer. Closed-loop stability of the proposed controller is established. Finally, simulation results including Monte Carlo simulations, nonlinear six-DegreeOf-Freedom(6-DOF) simulations and different ranges are presented to demonstrate the efficacy of the proposed control scheme. 展开更多
关键词 ADAPTIVE controller air-to-ground MISSILE Fixed-time ADAPTIVE REACHING law Fixed-time disturbance observer Model REFERENCE SLIDING mode control
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Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications
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作者 Minghui PANG Qiuming ZHU +4 位作者 Zhipeng LIN Fei BAI Yue TIAN Zhuo LI Xiaomin CHEN 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2022年第9期1378-1389,共12页
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
关键词 Line-of-sight probability model air-to-ground channel Machine learning Ray tracing
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Physical Layer Security for UAV Communications:A Comprehensive Survey 被引量:3
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作者 Jue Wang Xuanxuan Wang +5 位作者 Ruifeng Gao Chengleyang Lei Wei Feng Ning Ge Shi Jin Tony Q.S.Quek 《China Communications》 SCIE CSCD 2022年第9期77-115,共39页
Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an... Due to its high mobility and flexible deployment,unmanned aerial vehicle(UAV)is drawing unprecedented interest in both military and civil applications to enable agile and ubiquitous connectivity.Mainly operating in an open environment,UAV communications benefit from dominant line-of-sight links;however,this on the other hand renders the communications more vulnerable to malicious attacks.Recently,physical layer security(PLS)has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches.In this paper,a comprehensive survey on the current achievements of UAV-PLS is conducted.We first introduce the basic concepts including typical static/-mobile UAV deployment scenarios,the unique air-toground channel and aerial nodes distribution models,as well as various roles that a UAV may act when PLS is concerned.Then,we start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems,and extend the discussion to the more general scenario where the UAVs’mobility is further exploited.For both cases,respectively,we summarize the commonly adopted methodologies,then describe important works in the litera ture in detail.Finally,potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS. 展开更多
关键词 physical layer security UAV communications static/mobile UAV deployment air-to-ground channel trajectory optimization
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