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.

Unmanned aerial vehicle (UAV) communication is a promising part of next generation mobile communication network. Path loss (PL) is vital for the communication quality, where vegetation penetration loss (VPL) is a critical factor under UAV-to-ground (U2G) scenarios. In this paper, we propose a VPL model considering the foliage density and moisture content, and a measurement system is developed and implemented to acquire a large amount of channel data for fitting model parameters. Measurement results show that the proposed VPL model is more accurate and universal compared with typical models such as COST-235 and FITU-R. The proposed model and measurement results can provide a valuable reference for U2G communication in vegetation scenarios.