## 1 Introduction  
Internet service has experienced significant advancements in the past decade, enabling the expansion of infrastructure and increasing home access. Currently, Internet connectivity is essential for academic work, entertainment, and professional activities. A key factor in ensuring efficient transmission and reception of information between users is network coverage [1]. For years, attempts have been made to represent the behavior of electromagnetic waves, but the associated mathematical complexity, as exemplified by [PERSON]'s equations, is significant. Therefore, the development of models with simpler mathematical expressions is fundamental for the formulation of propagation equations using the empirical model proposed by [PERSON] in 1992 as a basis [2]. Over time, research has incorporated fading models, with notable contributions from [PERSON] and [PERSON] [3]. Models such as Rayleigh and Rice have been employed to represent propagation conditions in wireless systems for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. In environments requiring more sophisticated modeling, the Nakagami-m model is preferred due to its lower mathematical complexity compared to other models such as [PERSON] or Nakagami-q [4; 5; 6; 7; 8]. Recently, studies have proposed closed-form expressions for wireless channel modeling using probabilistic distributions, such as the Lomax and Log-Logistic distributions [9]. These distributions provide a physical interpretation for parameters that were initially not considered in the modeling of fading communication channels. This type of modeling with non-conventional distributions can be applied to more severe fading environments and offers simplified mathematics compared to the Two-Ray Fading Model [9; 10; 11]. Mathematical expressions for calculating wireless signal losses depend on factors such as distance, frequency, terrain irregularities, obstacles, diffraction, and signal reflection [12]. Propagation models are classified into empirical and deterministic types [13]. Empirical models are based on measurements from specific environments, and their accuracy in other settings depends on the similarity to the original scenario [14]. Deterministic models, on the other hand, are based on the laws of electromagnetism and radio wave propagation, including phenomena such as reflection, diffraction, and refraction [15]. In the literature, various studies combine empirical and deterministic models to address specific issues related to signal propagation and losses in wireless systems. These hybrid models integrate empirical and deterministic methods to improve path loss predictions in urban environments, using empirical approaches to calibrate the parameters of deterministic models. For example, in [16], the authors proposed a hybrid approach that combines empirical and deterministic models for radio propagation in metropolitan areas, focusing on the integration of empirical measurement data with simulations based on physical principles. Similarly, the work in [17] proposes a hybrid methodology that merges empirical and deterministic models to optimize wireless network planning, improving coverage and capacity accuracy. Works such as [18] present an integration of empirical and deterministic models for the prediction of coverage in 5G networks, aiming to optimize the precision of simulations compared to individual models. Recent studies on the Stanford University Interim (SUI) model have made significant advances in signal loss prediction, particularly for wireless network planning in complex environments. An analysis focused on applying the SUI model to a densely vegetated roadway, achieving a root mean square error (RMSE) of less than 5 dB through specific adjustments to improve the accuracy of the model in such challenging conditions [19]. In [20], the importance of integrating spatial data into signal path loss models is emphasized, addressing the limitations of traditional approaches such as the Stanford University Interim (SUI) model.