## 9 Simulation Results with SUI Model  
For the simulations conducted using the SUI model, the input parameters previously applied in earlier analyses were retained to ensure consistency and facilitate direct comparison. Simulations were performed for both 3.6 GHz and 6 GHz frequency bands in order to assess the frequency-dependent behavior of the model under identical environmental and system-level conditions. As the SUI model requires the categorization of terrain into types A, B, and C, and such classification was not readily available in the map database employed, this classification had to be carried out manually in advance using auxiliary datasets and expert judgement. A summary of the final terrain-type allocations and their correspondence with the simulation sites is presented in Table 10.  
The simulations were conducted within the MATLAB environment, where the SUI propagation model was implemented in accordance with its standard parametric formulation. MATLAB offers full flexibility in defining model parameters and controlling the simulation process; however, the reliability of the output remains closely tied to the resolution and fidelity of the input data--particularly with respect to terrain profiles, land cover characterization, and transmitter--receiver geometry. It should be noted that, while the model implementation provides a structured approach to propagation prediction, certain limitations remain. Notably, the static nature of the terrain representation and the absence of dynamic environmental parameters--such as seasonal variation in vegetation or temporal changes in surface conditions--can introduce uncertainties in the prediction results, particularly in complex or heterogeneous environments. To ensure reproducible results, the value of \(S\) (the shadowing component) was selected with particular care throughout the simulations. Modeling \(S\) as a log-normal random variable within the simulations introduces a stochastic element, resulting in non-reproducible outcomes. If the intention is to derive results by averaging the outputs of multiple simulation runs, then  
\[\lim_{N\to\infty}\frac{1}{N}\sum_{i=1}^{N}S_{i}=0, \tag{48}\] \begin{table}
\begin{tabular}{l l l} \hline
**Terrain Type** & **SUI Class** & **Justification** \\ \hline Water (salt) & C & Flat, open surface with very low reflectivity. \\ Water (fresh) & C & Same as salt water; smooth, open, low scattering. \\ Road/freeway & C & Paved open area with minimal terrain variation. \\ Bare land & C & Flat, undeveloped terrain with few obstructions. \\ Bare land/rock & B & Uneven rocky terrain with moderate scattering. \\ Cultivated land & B & Cropland with vegetation and mild reflections. \\ Scrub & B & Low, irregular vegetation with multipath effects. \\ Forest & A & Dense vegetation with high attenuation \\  & & and scattering. \\ Low dens. suburban & B & Sparse buildings, some reflection and obstruction. \\ Suburban & B & Medium-density area with partial obstruction. \\ Low dens. urban & B & Low-rise urban area with some open space. \\ Urban & A & Dense buildings with frequent obstructions. \\ Dens. urban & A & High building density with strong scattering. \\ High dens. urban & A & Very dense urban core with high loss. \\ High rise indust. & A & Industrial zone with strong metallic reflections. \\ Skyscraper & A & Dense high-rises with severe shadowing. \\ \hline \end{tabular}
\end{table} 
Table 10: SUI terrain classification of land cover classes with brief justification. due to the law of large numbers. This yields a deterministic result when \(S=0\) dB, which is reproducible, but fails to account for the potential impact of shadowing. To ensure both the reproducibility of simulation outcomes and the realistic representation of shadowing effects, fixed values of the shadowing loss component \(S\) were adopted for each terrain category. Specifically, values of 5 dB, 4.5 dB, and 4 dB were assigned to terrain types A, B, and C, respectively.