## 2 Air to Ground Channel Modelling  
Two primary methods are utilized for categorizing the air-to-ground channel: an empirical channel based on measurement data and a deterministic or geometry-based channel model derived from simulated data [9]. The empirical channel model used measurement data for C-band- and L-band-using aircraft in scenarios like suburban, urban, over-water, hilly, and mountainous environments [10; 11; 12; 13]. The data provided by measurement gives insight into the statistical proper ties of different terrains. In [14], the authors investigated the impact of airframe shadowing on the channel. They also provided a solution to mitigate this effect by effectively deploying multiple antennas. The statistical properties of large-scale and small-scale fading were analyzed using measurements for suburban areas, rural areas, and open areas of a city in the context of air-to-ground UAV communication, as detailed in [15; 16]. Determination of path loss for low-altitude UAV channels at frequencies 2.4 and 5.9 GHz was achieved by using a multilink measurement campaign [17]. The authors proposed path loss based on elevation angle and distance and further analyzed the shadow fading across different frequency bands and evaluated spatial correlations. A medium-sized UAV conducted low-altitude measurements in a suburban area to investigate multipath effects [18]. In Reference [19], the authors used a fixed-wing UAV for an air-to-ground channel measurement campaign conducted at low altitudes in a rural area, at 2.7 GHz. The paper offers details about the measurement system and analysis of large-scale fading, including path loss, shadow fading, and small-scale fading. The UAV provided a path loss model and identified the impact of altitude on the channel. While measurement data offer firsthand insights into channel characteristics, it is essential to note that such data are site-specific and may not be broadly applicable to general scenarios. Furthermore, specific measurement environments are complex and involve high costs. The deterministic channel modelling approach using ray tracing can demonstrate the channel's performance when considering environmental conditions. A ray tracing method was used to characterize a propagation channel model for rural, suburban, and dense urban scenarios at 28 GHz to find the effect of multipath components at the receiver [20]; suburban open-environment channel characteristics are analyzed in [21]. A ray tracing-based Monte Carlo method was used to analyze and evaluate UAV air-to-ground channel model performance [22]. Geometry-based stochastic models are widely used to evaluate spatial and temporal variation and characterize air-to-ground channels. A regular-shaped, geometry-based stochastic model for a UAV MIMO channel is proposed in [23]. The statistical characteristics of the channel were ascertained by considering parameters such as altitude and flight movement. A model with mixed bouncing was proposed to capture AG channel characteristics [24]. Taking into consideration the changing moving direction and speed of the MS, a channel model of 3D wideband MIMO was proposed [25], and the statistical properties were verified by simulation data and theoretical data. The models are tailored to specific geometries and unsuitable for different scenarios. A multi-UAV OFDMA communication system that utilizes geographical information develops a realistic channel model incorporating blockage-aware parameters [26]. In the existing body of literature, generic models are available to determine statistical parameters for modelling the UAV air-to-ground channel [27; 28; 29; 30; 31; 32; 33]. In [27], the authors introduce a generic statistical model tailored for low-altitude platforms, where the channel model parameters are derived via 3D ray-tracing at frequency bands of 700, 2000, and 5800 MHz for altitudes up to 2 km.