###### Abstract  
In this paper, we propose a geometric channel model for air-to-air (A2A) unmanned aerial vehicle (UAV) communication scenarios. The model is established by incorporating line-of-sight, specular reflection, and diffuse scattering components, and it can capture the impacts of UAV vibrations induced by the propeller's rotation. Based on UAV heights and ground scatterer density, a closed-form expression is derived to jointly capture the zenith and azimuth angular distributions of diffuse rays. The power of diffuse rays is modeled according to the grazing angle of the rays and the electrical properties and roughness of the ground materials. Key statistics, including the temporal autocorrelation function, spatial cross-correlation function, Doppler power spectrum density, and coherence time are derived, providing an in-depth understanding of the time-variant characteristics of the channel. The results indicate that the presented model is capable of capturing certain A2A channel characteristics, which align with the corresponding theoretical analysis. The findings suggest that the scattering effect of the A2A channel is significantly influenced by the altitude of the UAV. Additionally, it is shown that UAV vibrations can introduce extra Doppler frequencies, notably decreasing the temporal correlation and coherence time of the channel. This effect is more prominent when the system operates at high-frequency bands. The effectiveness of the presented model is confirmed through a comparison of its statistics with those of an existing model and with available measurement data. U  
A _Article_  
A Novel UAV Air-to-Air Channel Model Incorporating the Effect of UAV Vibrations and Diffuse Scattering  
[PERSON], [PERSON], [PERSON], W. A Novel UAV Vibrations and Diffuse Scattering  
A2G channel measurements were performed at 3.5 GHz within a campus environment. The results indicate the A2G channels consist of line-of-sight (LoS), ground reflection, and MPCs originating from ground scatterers. Variations over time in channel characteristics, such as path loss and MPC angles, were observed due to UAV flights. In addition, various studies have explored the dynamic movement of UAVs. For example, [PERSON] et al. proposed an A2G model that relaxed the linear trajectory condition of the UAV [6]. Specifically, the UAV was allowed to move with constant acceleration and angular speed. [PERSON] et al. reported a more practical model, which was devised by integrating a geometric model with the smooth-turn mobility model [7]. The latter was employed to generate UAV trajectories that accurately capture the aerodynamics of UAVs. In addition to exploring dynamic trajectories, several A2G channel models have been specifically crafted to account for the effects of UAV self-rotation [8; 9]. For instance, [PERSON] et al. proposed an A2G model that incorporated the UAV self-rotation effect using a rotation matrix [8]. This matrix updated the local coordinates of antennas after a three-dimensional (3D) rotation of the UAV. [PERSON] et al. adopted a comparable method, simulating the posture variation of the UAV [9]. The influences of UAV posture variation on channel behavior were investigated. However, it is worth noting that the rotation angles in these two models were represented as constant or linearly varying values, oversimplifying the depiction of UAV dynamics [8; 9]. A more realistic model was given by [PERSON] et al., where the pitch angle of the UAV was represented by a deterministic sinusoidal process, indicating that the amplitude and frequency of the pitch angle variations remain constant over time [10]. Furthermore, considering the impact of wind gusts, [PERSON] et al. described the random wobbling of the UAV using a stochastic sinusoidal process [11]. To mitigate computational complexity, this model merely considered the pitch angle of the UAV platform, limiting the vibrations to the vertical plane. In addition to the aforementioned approaches, [PERSON] et al.