## 11 Conclusions  
This study provided a detailed comparative analysis of the description, predictive accuracy, and terrain sensitivity of three widely adopted wave propagation models: ITU-R P.1546-6, SUI, and ITU-R P.452-17, benchmarked against the deterministic PEM model. The investigation was performed at 3.6 GHz and 6 GHz, using both test point and path-based simulations, and focused on evaluating the standard deviation of terrain height, received field strength (FST), the correlation between them, and key prediction error metrics such as RMSE, MAE, bias, and relative error (RelErr). At **3.6 GHz**, the comparison of ITU-R P.1546-6 and P.452-17 against the PEM baseline revealed marked differences in model behavior. The PEM model showed high terrain sensitivity, with a strong correlation between terrain variability and FST standard deviation (\(r=0.8053\) in flat terrain). The ITU-R P.1546-6 model, however, exhibited poor terrain responsiveness, including negative terrain correlation in flat regions (\(r=-0.5545\)), and large relative errors, particularly in hilly environments (up to **629%**). It displayed a strong _negative bias_ in flat and mixed terrain, consistently _underestimating_ the received field strength, which may result in overly conservative planning and inefficient frequency utilization. In contrast, the ITU-R P.452-17 model demonstrated excellent terrain correlation (\(r>0.9\)) and low relative error (e.g., RelErr = **9.17%** in flat areas), with minimal bias, indicating a reliable balance between physical realism and predictive stability. At **6 GHz**, where the SUI and ITU-R P.452-17 models were assessed, similar but more pronounced patterns emerged. The PEM model again served as a consistent terrain-sensitive reference (\(r=0.8229\) in flat terrain). The SUI model exhibited low terrain correlation (\(r=0.2716\)), very high relative errors (up to **217.74%**), and a strong _negative bias_, consistently _underpredicting_ signal strength across all terrain types. This systematic underestimation renders the SUI model unsuitable for accurate field strength prediction even over flat terrain and limits its use to coarse, conservative network planning scenarios where low computational complexity is prioritized over precision. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Type** & **Complexity** & **Main Calculation and Remarks** \\ \hline SUI & Empirical & \(\mathcal{O}(1)\) & Simple closed-form formula; minimal \\ ITU-R P.1546-6 & Empirical & \(\mathcal{O}(\log n)\) & Curve-based interpolation and corrections; \\ ITU-R P.452-17 & Hybrid & \(\mathcal{O}(n)\) & Terrin profile analysis (e.g., Bullington), diffraction, statistical losses \\ PEM (SSFM/CN-FDM) & Deterministic & \(\mathcal{O}(NM)\) or \(\mathcal{O}(N\log N)\) & Numerical PDE solving with FFT or matrix methods; high computation and memory demand \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Summary of the computational complexity and characteristics of the evaluated propagation models. Meanwhile, the ITU-R P.452-17 model retained strong terrain responsiveness (\(r=0.9134\)) and maintained low relative errors in flat terrain (around **9%**). However, in urban and mountainous environments, its relative errors increased substantially (up to **303%**), and it exhibited a _moderate positive bias_, slightly _overestimating_ field strength. This overestimation may still be advantageous in conservative interference assessments but reduces the model's reliability for precise coverage predictions in complex terrains at higher frequencies. The main conclusions of the study are summarized below:  
* The **PEM model** consistently demonstrated the strongest terrain sensitivity and served as a physically robust reference across frequencies. Its high terrain correlation makes it ideal for AI training, measurement validation, and high-accuracy modeling tasks.