## 6 Discussion of the Results  
First, a key aspect of the study is that when comparing the signal levels of the hotspot and the WiFi repeater (Raspberry Pi 3), it was observed that the adjusted model for the hotspot (Figure 7) exceeded the field-measured levels, especially beyond 60 m. At a power level of \(-\)70 dBm, the data began to approach the adjusted model. Furthermore, the heat maps in Figures 9 and 11 did not match because the hotspot has a higher transmission power than the WiFi repeater, resulting in different signal levels. Furthermore, the reception levels of the hotspot were low at distances close to the transmitter, as seen in Figure 5, which led to a new data collection on the WiFi repeater. It is noted that, when comparing the signal levels of the hotspot and the WiFi repeater (Raspberry Pi 3), the adjusted model for the hotspot (Figure 7) exceeded the measured field levels, especially beyond 60 m. At a power level of \(-\)70 dBm, the data began to approach the adjusted model. Furthermore, the heat maps in Figures 9 and 11 did not match because the hotspot has a higher transmission power than the WiFi repeater, resulting in different signal levels. Furthermore, the reception levels of the hotspot were low at distances close to the transmitter, as seen in Figure 5, which led to a new data collection at the WiFi repeater. Another noteworthy aspect is that in Figure 15, the curves of the two models adjusted by linear and logarithmic regression for the WiFi repeater reception levels fall within the range of \(-\)40 dBm to \(-\)98 dBm, with a sample accumulation starting at 60 m, particularly between the levels of \(-\)75 dBm and \(-\)98 dBm. However, it is evident that the logarithmic  
Figure 15: Model fitting graph based on the logarithmic regression method. Parameters: base height \(h_{b}=5\) m, receiver height \(h_{m}=1.7\) m, frequency \(f=2.412\) GHz, and suburban areas Category C.  
model fits the samples better than the linear model. For this reason, when using the logarithmic model to determine path loss, the path loss exponent (\(\gamma\)*10) is 8.96, a value that matches the one obtained in Equation (14), demonstrating an appropriate fitting process. The results obtained align with previous studies presented in Table 1, which focuses on adjusting propagation models such as the SUI to improve accuracy in complex environments at various distances, frequencies, and technologies. It is important to note that the process of adjusting a model can encounter several challenges that need to be addressed during implementation. In this regard, the use of reference models and mathematical tools such as MATLAB is crucial to improve accuracy and optimize these models. The logarithmic model adjustment presented through Equation (22) is used to predict signal loss, demonstrating particular advantages at short distances (0 to 100 m). These adjustments improve prediction accuracy and optimize network planning in local areas, providing the Universidad de las Americas with a valuable tool to generate heat maps in parishes with conditions similar to those of Checa. The results of the adjusted model (in blue) shown in Figure 15 demonstrate the path loss while adjusting for the effects of obstacles and topography, particularly at short distances (18 m). In this range, the adjusted model is more sensitive to these factors compared to other models, such as Okumura-Hata and COST 231-Hata. However, as the distance increases (from 18 to 55 m), the adjusted model stabilizes and aligns more closely with the COST 231-WI and Okumura-Hata models, indicating that environmental effects diminish with distance. Beyond 55 m, the adjusted model closely matches these models, whereas the free-space model exhibits significantly lower levels. Considering work [26], the importance of adjusting existing theoretical models to better represent the real conditions of signal propagation in specific areas is emphasized.