3D non-stationary geometry-based stochastic model for unmanned aerial vehicle air-to-ground multi-input multi-output channels

A three-dimensional non-stationary geometry-based stochastic model for unmanned aerial vehicle(UAV)air-to-ground multi-input multi-output(MIMO)channels is proposed.The scatterers surrounding the UAV and ground station are assumed to be distributed on the surface of two cylinders in the proposed model.The impact of UAV rotations and accelerated motion is considered to describe channel non-stationarity.The computational methods of the corresponding time-variant parameters,such as UAV antenna array angles,time delays,and maximum Doppler frequencies,are theoretically deduced.The model is then used to derive channel statistical properties such as space-time correlation functions and Doppler power spectral density.Finally,numerical simulations are run to validate the channel s statistical properties.The simulation results show that increasing the UAV and ground station accelerations can reduce the time correlation function and increase channel non-stationarity in the time domain.Furthermore,the UAV s rotation significantly influences the spatial correlation function,with rolling having a greater influence than pitching.Similarly,the different directions of UAV movement significantly impact the Doppler power spectral density.

Channel Modeling for Air-to-Ground Wireless Communication

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.

Fixed-time adaptive model reference sliding mode control for an air-to-ground missile

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.

Machine learning based altitude-dependent empirical LoS probability model for air-to-ground communications

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.

Physical Layer Security for UAV Communications:A Comprehensive Survey

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.