# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 3 SSCM-RT Channel Model
### Modeling Scenario Characterization  
The SSCM-RT model divides long-distance propagation into two parts, using the ray tracing model for deterministic modeling in the portion of the propagation distance close to the receiver and the spatial statistical channel model in the portion of the propagation distance far from the receiver. The electromagnetic wave's polarization angle may shift when the wavelength is close to the diameter of a raindrop, complicating the calculation. In the long-distance transmission process, the spatial statistical channel model can be used to calculate the influence of atmospheric attenuation on the channel more simply and reduce the complexity of the ray tracing technique computation in this section of the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Correlation Distance (m)**} & \multicolumn{2}{c}{**Rural Macro**} & \multicolumn{3}{c}{**Urban Micro**} & \multicolumn{3}{c}{**Urban Macro**} & \multirow{2}{*}{**Indoor**} \\ \cline{2-2} \cline{4-9}  & **LOS** & **NLOS** & **O2I** & **LOS** & **NLOS** & **O2I** & **LOS** & **NLOS** & **O2I** \\ \hline Cluster and ray specific & \multirow{2}{*}{50} & \multirow{2}{*}{60} & \multirow{2}{*}{15} & \multirow{2}{*}{12} & \multirow{2}{*}{15} & \multirow{2}{*}{15} & \multirow{2}{*}{40} & \multirow{2}{*}{50} & \multirow{2}{*}{15} & \multirow{2}{*}{10} \\ random variables & & & & & & & & & & \\ LOS/NLOS state & & 60 & & & 50 & & 50 & & 10 \\ Indoor/Outdoor state & & 50 & & & 50 & & 50 & & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation distance for spatial consistency. Figure 3: The relationship of path loss exponent and frequency by using the statistical channel model. attenuation. The ray tracing model can guarantee a more precise characterization of the multipath component of the dynamic channel at close range. The specific implementation and guiding concepts of SSCM-RT channel modeling are as follows:  
In this section, assume that the low-altitude UAV flies at a constant altitude and makes a circular motion with a radius of 100 m. Assume that the high-altitude UAV is stationary relative to its circular motion, and the projection point to the plane where the low-altitude UAV is located is at the center of the circle, as shown in Figure 4. The channel is divided into two parts of the propagation process. One is the long-distance propagation part. This mode of motion presents symmetry. The use of statistical channel modeling can better describe the environmental impact such as atmospheric attenuation, and set the distance of this part as \(d_{1}\). The other is the close-range propagation part. The model, morphology, and motion of the UAV will have a certain influence. Therefore, the deterministic modeling method can obtain more accurate dynamic data, and set the distance of this part as \(d_{2}\). Five channel modeling effects can be offered for comparison in this scenario, matching the statistical channel parameter values, including the SSCM model only, the RT model only, the 3 GPP-GSCM model, the simple prorate model, and the SSCM-RT hybrid model proposed in this study. The traditional two-path ground reflection model is not applicable for inter-UAV communication and it is challenging to cancel the other multipath components in the actual air-to-ground channel with small errors; thus, no comparison is made in this paper. The 3 GPP-GSCM model is a traditional geometry-based random channel model. In this paper, the time-varying channel model constructed by 3 GPP TR38.901 is used as a comparison. Simple prorate modeling is based on the cascade channel modeling method from the reference [15]. Path loss in this reference can be written as  
\[ \begin{array}{ll}PL(CAS)&=\omega P_{L}(far-field)+(1-\omega)P_{L}(near-field )\\ &=\frac{c^{2}}{\left(\frac{\omega}{\Delta}\right)^{2}d_{1}^{2}d_{2}^{2}}e^{- j\frac{2\alpha}{\lambda}(d_{1}+d_{2})}+\frac{c^{2}}{\left(\frac{\omega\pi}{ \lambda}\right)^{2}(d_{1}+d_{2})^{2}}e^{-j\frac{2\omega\pi}{\lambda}(d_{1}+d_{ 2})},\end{array}