## 3 Results and Discussions
### Typical Samples of the Raw Data Collected During the Measurement Campaign
#### 3.3.3 Error Analysis in the Akure Environment  
Table 10 presents the results of the performance evaluation metrics of the four models. The COST-231 model had the best performance with the smallest RMSE, minimum error, mean error, and skewness values of 9.877, 0.308, 7.343, and 117.025, respectively. The Hata model was the next in performance, with an RMSE of 11.799. These findings show that both the COST 231 and Hata models are fit for predicting path loss over a digital terrestrial UHF signal in the suburban city of Akure. This is based on the fact that their RMSE values were comfortably within the 7-15 dB range, which is acceptable for suburban cities [2]. However, COST-231, with the smallest RMSE, is preferred for the study area. Ericsson and ECC-33 are not fit for use in this environment based on their inaccurate predictions, mainly due to out-of-range RMSE values. Figure 7 presents the variation in the RMSE for the models. Optimization of Hata and COST-231 Models for Better Performances in Ikorodu and Akure Environments, Respectively  
These models were optimized using the logarithmic linear models presented in (18)-(20). The logarithmic constants for the Hata model, being the most suitable for prediction in Ikorodu based on its smallest RMSE, were computed to be \(A=7.876\) and \(B=-3.504\). The derived logarithm linear model is presented in (21):  
\[Y=[7.876-3.504\text{log}(d)] \tag{21}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Error Metrics** & **COST-231** & **HATA** & **Ericsson Model** & **ECC-33 Model** \\ \hline RMSE & 9.877 & 11.799 & 80.150 & 190.642 \\ Maximum error & 26.266 & 21.136 & 95.388 & 202.635 \\ Minimum error & 0.308 & 0.463 & 17.225 & 154.994 \\ Mean error & 7.343 & 9.832 & 18.570 & 190.337 \\ \hline Skewness & 117.025 & 157.313 & 1213.145 & 3045.397 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Error analysis of the PPLMs in Akure. Figure 7: Variation in RMSE for the predicted models in Akure environment. Equation (21) was added to the main Hata equation to obtain the optimized Hata model \(\left[L_{(urb_{Ops})}\right]\) for prediction in the Ikorodu environment, as presented in (22). This model was used to re-compute the path loss over the study area, with error analysis carried out between the measured data and the optimized model. The optimized model gave an improved RMSE of 5.895 compared to 10.810 of the untuned model, which is a significant improvement. The optimized model is presented in (22) and recommended for prediction in Ikorodu or any similar tropical environment. \[L_{(urb_{Ops})}=\left[ \begin{array}{c}69.55+26.16\text{log}(f)-13.82\text{ log}\left(h_{b}\right)-\alpha(h_{r})\\ +(44.9-6.55\text{log}\,h_{b})\text{log}(d)+(7.876-3.508 log(d))\end{array} \right] \tag{22}\]  
where  
\(f\) is the carrier frequency, 150-1500 MHz;  
\(h_{b}\) is the transmitting base station's tower or mast height, ranging between 20 and 200 m;  
\(h_{r}\) is the receiver antenna's height, ranging from 1 to 10 m;  
\(d\) is the distance between the transmitter and the receiver (Tx-Rx), which ranges from 1 to 20 km;  
\(a(h_{r})\) is the correction factor for the receiver height and is as given in (4). Similarly for the COST-231, \(A\) and \(B\) were determined to be 6.550 and \(-\)5.862, respectively. The derived logarithmic model for COST-231 is presented in (23) and was added to the main COST-231 model, as presented in (24). \[Y=\left[6.550-5.862\text{log}(d)\right] \tag{23}\]  
The optimized COST 231-Model \(L_{(OPZ-COST)}\) is as follows:  
\[L_{(OPZ-COST)}= \begin{array}{c}\left\{46.3+33.9\text{log}_{10}(f)-13.82\text{ log}_{10}(h_{b})+\right.\\ \left[44.9-6.55\text{log}_{10}(h_{b})\right]\text{log}_{10}(d)-\alpha(h_{r})+ \left[6.550-5.862\text{log}(d)\right]\right\}\end{array}  \tag{24}\]  
where all parameters are as previously defined, and \(C_{m}=0\) for the suburban city of Akure. This model was again used to re-compute the path loss in the Akure environment.