## 1 Introduction
Using machine learning techniques, including multilayer perceptron neural networks, significant improvements in path loss prediction accuracy were achieved in urban and suburban environments. The results indicate that GeoAI-based models outperform empirical models in terms of accuracy and generalization, suggesting a promising direction for optimizing cellular communication networks. In [21], empirical measurements were carried out in outdoor environments within the 2.4 GHz band. This finding underscores the need to adjust a model and its applicability to specific terrains, taking into account factors such as vegetation and surrounding structures. Upon comparing different models, the measured model and the COST 231 Walfisch-Ikegami model were adopted for network planning, indicating that customizing the SUI model can enhance the prediction accuracy in areas with complex geographical characteristics. In this context, the Universidad de Las Americas, as part of a community outreach project, aimed to improve the Wi-Fi coverage in the parish of Checa. Students enrolled in the Hardware for Telecommunications Systems course designed a wireless signal repeater using a Raspberry Pi 3 module, which connected to a hotspot of the free wireless network provided by the Municipality of the Metropolitan District of Quito through a wireless communication bridge. This study introduces an adjustment to the Stanford University Interim Propagation Model (SUI) by using signal strength data collected from both the hotspot and the Wi-Fi repeater module. Empirical and deterministic data were integrated to generate a heat map of the repeater installed in the central park of Checa using MATLAB for analysis. A review of recent literature on the SUI model, as seen in Table 1, generally shows its application over distances greater than 100 m and frequencies other than 2.4 GHz. However, in study [21], an experimental model adjustment was performed based on empirical outdoor measurements in the 2.4 GHz band. Through a comparison of various models, both the measured model and the COST 231 Walfisch-Ikegami model were adopted for network planning, emphasizing the importance of adapting network design to local characteristics for effective communication. Customizing the SUI model can enhance prediction accuracy in areas with geographical characteristics similar to Checa, thus facilitating coverage planning for nearby areas like Pifo and Tumbaco where additional repeaters are to be deployed for community benefit, ultimately contributing a new experimental model to scientific literature. Furthermore, the use of heat maps enables visualization of signal coverage emitted by wireless devices [22], representing signal intensity on a color scale which is essential for evaluating network performance in terms of reception levels. This model adjustment aims to serve as a tool to determine the optimal repeater location, maximizing efficiency, and improving connectivity in the area. This initiative is part of the community outreach efforts of the Universidad de las Americas and its students to improve coverage and deliver optimal results in the parishes of the Metropolitan District of Quito. The paper schedule is as follows. Section 2 briefly presents the description of the problem and the methodology employed. Then, Section 3 shows the performance analysis of the system in detail. Next, Section 4 describes the adjustment scheme of the propagation model using least squares linear regression. Finally, the conclusions in Section 8 summarize the key points and suggest future research directions.