###### Abstract
conducted an evaluation of UAV millimeter-wave links, where UAVs were equipped with planar arrays [12]. The boresight direction of the array was assumed to follow a Gaussian distribution. The impact of antenna misalignment loss on system performance was analyzed. Additionally, a more intricate model was presented by [PERSON] et al., where the roll and pitch rotations of the UAV were modeled using a Gauss-Markov model [13]. It is essential to note that both the Gauss and Gauss-Markov wobbling models have not yet been validated through measurements. In general, the aforementioned models primarily focus on the rotation of the UAV, indicating that the UAVs undergo a turning or rotational motion [8; 9; 10; 11; 12; 13]. The UAV rotation may be relevant for reflecting the influence of atmospheric turbulence and wind gusts. However, when considering the operations of propellers and engines, both the UAV and antennas may experience high-frequency repetitive movements [14]. This refers to oscillating or back-and-forth motions around an equilibrium position, rather than spinning motions. Measurements indicate that the rotation of UAV propellers generates distinct Doppler frequency shift patterns in radar returns, markedly different from the Doppler characteristics of slow-moving scatterers like trees and birds. In [15], a technique for UAV detection and tracking was proposed utilizing micro-Doppler signatures from a multistatic radar system. The results demonstrated that micro-Doppler features are effective in distinguishing UAVs from clutter, especially when the UAV is hovering or moving with low radial velocities. Note that the authors focused on UAV detection and tracking, rather than channel modeling. Regarding the propagation scenario, the existing literature predominantly focuses on A2G scenarios in UAV channel modeling. However, only a limited number of UAV channel models are suitable for air-to-air (A2A) scenarios, with most of them emphasizing the larger-scale fading of the A2A link. [PERSON] et al. conducted a preliminary measurement for A2A communications [16]. Their findings revealed a decrease in both multipath and shadowing fading as the UAV altitude increases. Furthermore, they introduced a modified log-distance path-loss model. Similar investigations were undertaken by [PERSON] et al. and [PERSON] et al., who estimated path-loss exponents and proposed path-loss models for A2A communications based on measurement and ray-tracing results [17; 18]. In general, A2 Achannel modeling on small-scale fading is still in its early stages, with most studies relying on classical geometric models such as the ellipsoid model [19], two-sphere model [20], and cylinder model [21]. Furthermore, when a wave encounters rough surfaces or small objects relative to wavelength, the incident wave is split into a specular ray, which follows Snell's Law, and multiple diffuse rays that scatter in various directions [22]. The proportion of diffuse components in the received power is determined by the grazing angle of the wave, the roughness of the surface, and the electrical properties of reflective materials. Measurements conducted at 1.35 GHz in an industrial scenario indicated that the diffuse components account for 15-80% of the channel power [23]. Similarly, measurements conducted at 5.2 GHz revealed that the proportion of diffuse component power in the total received power can achieve a maximum of 90% in urban non-LoS (NLoS) scenarios [24]. In UAV communications, large altitudes of the UAV result in large grazing angles. This means a large amount of energy can be scattered instead of reflected. However, the above-mentioned UAV models were obtained by summing discrete multipath components originating from large discrete objects, and the contribution of the diffuse components was neglected, which may lead to an underestimation of the received power and channel capacity [25].