Drones are expected to be promising aerial platforms in air-to-ground (A2G) integrated communication networks, where the A2G propagation channel is fundamental for reliable communication links. This paper proposes a hybrid parameter generation framework combining the deterministic and statistical methods for a cluster-based A2G channel model. In this framework, the map-based deterministic method is used to generate delay and angle parameters, which can achieve great scenario consistency. However, it is difficult for users to provide precise material information of scatterers, which would cause deviation of power parameters. To tackle this issue, a measurement-driven power generation method is proposed. Firstly, a bandwidth-dependent clustering method is developed to group the rays into clusters. Then, the cluster power is generated by measurement-driven statistical models with a power-decomposition idea. It decomposes the power parameter into several parts that are less dependent on the scenario. Moreover, it can avoid massive measurement campaigns and is more robust when applied in unmeasured scenarios. Finally, a new channel measurement campaign in a street canyon scenario is performed for validations. The proposed method is also compared with a ray-tracing (RT) method and the 3rd Generation Partnership Project (3GPP) channel model. It is shown that the proposed framework and power generation method are great alternatives for accurate and robust modeling requirements under specific A2G communication scenarios.

Unmanned aerial vehicle (UAV) air-to-air (A2A) communications are emerging as a vital component of future low-attitude wireless networks. This paper 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 UAV 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.

In the increasingly critical field of aerial communication, unmanned aerial vehicles (UAVs) have gained significant attention as prominent representatives, and accurate air-to-air (A2A) channel modeling plays a pivotal role in the design and evaluation of reliable communication systems. This paper presents a A2A channel model for UAV communications. It introduces a quasi-deterministic approach to address limitations in existing modeling frameworks. The proposed model uses a truncated ellipsoid to capture the distribution of scatterers in A2A scenarios and, for the first time, incorporates rooftop specular reflection (RSR). Power correction factors, based on the UAV’s airframe structure, position, and posture, are introduced to provide a comprehensive and realistic depiction of the A2A communication channel. The performance of proposed model is assessed by simulating key statistical channel characteristics and comparing with other alternatives. The simulations illustrate how channel behavior is influenced by factors such as flight level, flight trajectory, and UAV posture. The results show that RSR leads to the channel hardening effect, while airframe occlusion causes the received signal power to vary gradually with changes in UAV’s position and posture. The validity of the model is confirmed through comparison with measurement data and ray-tracing results, proving its accuracy and practical application.

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.

The optimization of line-of-sight (LoS) probability is essential for enhancing 6G unmanned aerial vehicle (UAV)-to-ship (U2S) communication in maritime scenarios, where the communication channel is affected by dynamic sea waves (SW) and six-dimensional (6D) transceiver positioning. Traditional models have been found inadequate in addressing these variables. In this paper, a novel LoS probability model is introduced, integrating dynamic SW and 6D positioning of ships and UAVs. The effects of SW height, number, and period on LoS blockage are quantified, providing a tailored approach for maritime environments. A dual-channel convolutional neural network (DCCN) coupled with spider monkey optimization (SMO) is applied to refine the complex relationship between environmental factors and LoS probability. The model, trained with ray tracing (RT) data, is shown to significantly outperform standard models like 3GPP and 5GCM, offering new insights into U2S communication in maritime scenarios.

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.

This paper proposes a general unmanned aerial vehicle (UAV)-to-ground (U2G) channel model. The proposed model is consistent with real scenarios by considering the impacts of flight altitude and UAV posture on channel gain. Machine learning and ray tracing (RT) techniques are employed to improve the generation method of altitude-dependent parameters, i.e., path loss (PL) and shadow fading (SF). In addition, posture-related fuselage shadowing coefficient (FSC) is introduced to modify the channel gain, and three-dimensional (3D) geometry modeling of the fuselage is conducted to calculate the FSC. Numerical simulation results show that the flight altitude and UAV posture have obvious effects on channel gain. The proposed model with modified channel gain can effectively describe the PL, SF, and received power under fuselage shadowing. The validity and advantage of the improved channel gain are verified by comparing the simulation results with the measured ones.

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.

In this paper, a novel path loss (PL) prediction model is proposed for the obstructed-line-of-sight (OLoS) and non-line-of-sight (NLoS) paths in unmanned aerial vehicle (UAV) communication over vegetation areas. The proposed PL prediction model is designed based on a deep neural network (DNN) with a pre-training module (PTM). We pre-train the DNN by ray tracing (RT) simulation data and then optimize the network by sparse measurement data, which can significantly reduce the demand for measurement data. Moreover, PL measurements over vegetation areas are carried out at 2 GHz on the campus to validate the proposed model. It is shown that the prediction results of the proposed model are in good agreement with the measurement data and the ones of the fitted International Telecommunication Union recommendation (FITU-R) model under the OLoS case. Moreover, the proposed model is more general and suitable for air-to-ground (A2G) communications by considering the impact of the wide range of reflection angle (RA) variations on the PL.

Most existing cluster-based cross-domain person re-identification (re-id) methods only pre-train the re-id model on the source domain. Unfortunately, the pre-trained model may not perform well on the target domain due to the large domain gap between source and target domains, which is harmful to the following optimization. In this paper, we propose a novel Self-supervised Pre-training method on the Target Domain (SPTD), which pre-trains the model on both the source and target domains in a self-supervised manner. Specifically, SPTD uses different kinds of data augmentation manners to simulate different intra-class changes and constraints the consistency between the augmented data distribution and the original data distribution. As a result, the pre-trained model involves some specific discriminative knowledge on the target domain and is beneficial to the following optimization. It is easy to combine the proposed SPTD with other cluster-based cross-domain re-id methods just by replacing the original pre-trained model with our pre-trained model. Comprehensive experiments on three widely used datasets, i.e. Market1501, DukeMTMC-ReID and MSMT17, demonstrate the effectiveness of SPTD. Especially, the final results surpass previous state-of-the-art methods by a large margin.

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 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 (3GPP) 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 A2G scenarios (not included in this paper) by adjusting the parameters according to the simulation or measurement data.

Based on the geometry and ray tracing (RT) theory, a millimeter wave (mmWave) channel model and parameter computation method for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communications are proposed in this paper. In order to speed up the parameter calculation, a reconstruction process of scene database on the original digital map is developed. Moreover, the effects of reconstruction accuracy on the channel parameter and characteristic are analyzed by extensive simulations at 28 GHz under the campus scene. The simulation and analysis results show that the simplified database can save up to 50% time consumption. However, the difference of statistical properties is slight in the campus scenario.