## 4 Results and Discussion
### RSRP and RSRQ Prediction Models  
Tables 5 and 6 present the performance of the RSRP and RSRQ prediction models under LARS lasso, SVR, polynomial, and logarithmic methods, respectively. Table 5 also compares the results obtained in this study against a few related works. Results show that the performance of the proposed models outperformed other related works. Meanwhile, the performance of LARS lasso and SVR are almost the same, while SVR involves high computational complexity and requires more computation power. Hence, LARS lasso is selected as a linear regression method with acceptable accuracy. The results of the polynomial and logarithmic methods show that the polynomial degree of 2 could present slightly better results than the linear methods and the logarithmic method outperforms linear regression models. That latter comes since it uses nonlinear functions to train the data and generates a model that fits a much wider range of data. On the other hand, the performance of a polynomial method depends on its degree, in which higher degrees enhance the flexibility of the model to fit a broader range of data. However, the overfitting issue should be considered, where the generated method may fit perfectly with the existing data but may not predict accurate output for new data. In this regard, Table 7 compares the evaluation results of the polynomial-based RSRP model under different degrees. Results thereby show that increasing the degree to 6 improves the performance slightly. Then, due to the overfitting, the performance starts to degrade. Hence, due to the simplicity of the equation, we believe that a degree of 2 can provide accurate enough prediction methods for RSRP and RSRQ. The following equations present the proposed multiple linear regression models for RSRP and RSRQ as a function of 2D distance in meter and elevation angle in degree. The angle coefficients showed that RSRQ is more angle-dependent than RSRP because of the higher interference level at higher altitudes. Figures 11 and 12 depict a 3D representation of the linear regression-based RSRP and RSRQ prediction models, respectively. \[\text{RSRP}_{\text{linear}}=-68.197-0.0264\times d_{2D}-0.0057\times\beta, \tag{12}\]  
\[\text{RSRQ}_{\text{linear}}=-7.7894-0.012\times d_{2D}-0.0279\times\beta\,. \tag{13}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polynomial Degree** & **RMSE** & **MAPE (\%)** & **MedAE** \\ \hline
**2** & 4.49 & 4.8 & 2.90 \\
**4** & 4.33 & 4.6 & 2.82 \\
**6** & 4.31 & 4.6 & 2.80 \\
**8** & 4.39 & 4.7 & 2.84 \\
**10** & 4.48 & 4.8 & 2.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of polynomial method under different degrees. Figure 11: 3D representation of the proposed linear regression model for RSRP. The following equations present the proposed polynomial-based RSRP and RSRQ prediction models, and Figures 13 and 14 show a 3D representation of the proposed models for the RSRP and RSRQ, respectively. \[\text{RSRP}_{\text{polynomial}}=-65.048-4.67\times 10^{-2}\times d_{2D}+0.11 \times\beta+3.22\times 10^{-5}\times d_{2D}^{2}-4.35\times 10^{-4}\times d_{2D} \times\beta-2.04\times 10^{-3}\times\beta^{2}, \tag{14}\] \[\text{RSRQ}_{\text{polynomial}}=-4.679-2.66\times 10^{-2}\times d_{2D}-3.83\times 10^{-2}\times\beta+1.95\times 10^{-5}\times d_{2D}^{2}-1.47\times 10^{-4} \times d_{2D}\times\beta-3.13\times 10^{-4}\times\beta^{2}. \tag{15}\]  
Figure 12: 3D representation of the proposed linear regression model for RSRQ. Figure 13: 3D representation of the proposed polynomial model for RSRP. Equations (16) and (17) present the proposed logarithmic-based models for RSRP and RSRQ, and Figures 15 and 16 depict a 3D representation of the proposed models for RSRP and RSRQ, respectively. \[\text{RSRP}_{\text{logarithmic}}=-30.75-8.11\times\log_{10}(d_{2D})+0.07\times \log_{10}(\beta), \tag{16}\]  
\[\text{RSRQ}_{\text{logarithmic}}=9.91-3.76\times\log_{10}(d_{2D})-0.017\times \log_{10}(\beta). \tag{17}\]  
Figure 14: 3D representation of the proposed polynomial model for RSRQ.