Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role in reliable communications between UAV and ground terminal. This paper proposes a three-dimensional (3D) non-stationary hybrid model including large-scale and small-scale fading for U2G multiple-input-multiple-output (MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e., 3D trajectory and posture of UAV, fuselage scattering effect (FSE), and posture variation fading (PVF) are incorporated into the proposed model. The channel parameters, i.e., path loss (PL), shadow fading (SF), path delay, and path angle, are generated incorporating machine learning (ML) and ray tracing (RT) techniques to capture the structure-related characteristics. In order to guarantee the physical continuity of channel parameters such as Doppler phase and path power, the time evolution methods of inter- and intra- stationary intervals are proposed. Key statistical properties, including temporal auto-correction function (ACF), power delay profile (PDP), level crossing rate (LCR), average fading duration (AFD), and stationary interval (SI), are analyzed with the impact of the change of fuselage and posture variation. It is demonstrated that both posture variation and fuselage scattering have crucial effects on channel characteristics. The validity and practicability of the proposed model are verified by comparing the simulation results with the measured ones.

A realistic channel model is vital for designing, optimizing, and evaluating unmanned aerial vehicle (UAV)-to-ground (U2G) communication systems. This paper presents a comprehensive U2G ultra-wideband (UWB) channel model by considering the large-scale fading (LSF), including path loss (PL), shadow fading (SF), and airframe shadowing (AS). The effects of carrier frequency, bandwidth, building distribution, and UAV airframe structure on the LSF are fully studied. Different from the traditional bandwidth-independent PL calculation method, an extended closed-form expression for calculating PL is proposed to capture the new characteristics of ultra-wide bandwidth. The SF part is statistically described by a parametric lognormal model, and the built-up scenario-dependent parameters are predicted by a hybrid method with the ray-tracing and virtual scenario technologies. In addition, the AS part with respect to the airframe structure and posture variation is derived. It consists of the direct and reflected path components which are obtained by the deterministic and statistical approaches, respectively. The numerical simulations show that bandwidth, building distribution, and airframe structure have great influence on the LSF characteristics. For example, the fluctuation of received power can reach 30 dB due to the AS. The proposed model also shows its effectiveness and reliability by comparing with the RT and measured data, as well as the good compatibility with standardized models for some typical scenarios.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial large-scale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones.

A general empirical path loss (PL) model for air-to-ground (A2G) millimeter-wave (mmWave) channels is proposed in this paper. Different from existing PL mo

Autonomous aerial vehicle (AAV) air-to-air (A2A) communications are emerging as a vital component of future low-attitude wireless networks. This article introduces a novel A2A channel model and evaluates its statistical properties through analysis and experimental validation. The proposed model adopts a quasi-deterministic approach that incorporates rooftop specular reflection (RSR), distinguishing it from traditional ground reflection. Airframe occlusion (AO) is represented using a diffraction-based segmented function to accurately characterize its impact on the line-of-sight path. Key statistical indicators are derived based on the proposed model, and numerical results show that the presence of RSR reduces both the level crossing rate and average fade duration. Enhanced Rician factor improves spatial-temporal correlation, while higher transmission power and lower AAV mobility reduce outage probability. Compared to standardized models, the proposed model demonstrates improved capability in capturing the effects of RSR and AO while maintaining compatibility. Additionally, an A2A channel measurement platform leveraging the high-autocorrelation properties of Zadoff-Chu sequences is developed to extract channel impulse response. Experimental measurements of channel capacity, outage probability, and root mean square delay spread closely align with simulated results, validating the model’s accuracy and reliability.