## 1 Introduction
The majority of physical models are simple to use but their assumptions are based on many simplifications. Generally, for this reason, they are employed to describe the phenomenon within a given error, whereas empirical models take valuable and building-specific information into account. As a result of combining the previous approaches, hybrid models include the accuracy of physical models and the suitability of measurement-based tuning perform in empirical models. On this basis, measurement-based methods are promising to achieve accurate and practical predictions, even in situations where there are not enough samples to carry out a rigorous characterization. To address the shortcoming of samples from measurement campaigns, linear geostatistics demonstrate their usefulness to predict unknown data with few available samples for practical designs. To quantify the reliability of coverage provided by any radio cell it is imperative to understand and to characterize median path loss, shadow fading, and fast fading as the main path loss components [12]. As is surveyed in [11], the estimation of median path loss is deterministic, mathematical models describe it in terms of how path loss changes as a function of some factors, such as frequency and specific distance, etc. Nevertheless, these models need to account for the shadowing process, including it as an additional variable which describes the dispersion with respect to the nominal value given by the path loss models. Therefore, due to the normal distribution that shadowing produces in the signal measured, the most traditional action is to characterize it by a zero-mean Gaussian random variable, such as in [6]. Other wireless studies include nonconstant shadowing variance and non-lognormal shadowing, and predict the variance by considering the correlation between paths [13; 14]. However, in [15] the authors validate a more accurate method to estimate the spatial correlation of shadowing by including Kriging, a linear geostatistics technique that is based on the regionalized theory, which states that there is a variance rate between samples over space in a physical continuity context, i.e., the spatial dependence stated by [PERSON] in [16]: spatial samples taken close to each other may be expected to have more similar values than samples taken farther apart. There are different types of Kriging techniques, ordinary Kriging is the most common method; however, if there is a spatial trend then this technique is no longer the appropriate to model the spatial variability. There are other alternatives of Kriging, for instance, universal Kriging and simple Kriging, among others. The properly selection of the Kriging technique is focused on the data characteristics. To summarize, the aim of Kriging is to minimize the variance of estimation errors under the constraint of unbiased estimation [17]. The spatial prediction of Kriging does not require that data to be interpolated follow a normal distribution since Kriging describes the best linear unbiased estimator in the sense of least variance. However, if the data follow a normal distribution, then Kriging becomes the best unbiased spatial predictor. Therefore, telecommunications studies include Kriging to realize a highly accurate radio environment map (REM) [18; 19; 20] or to enrich the training dataset (to produce a large amount of data) for channel modeling, as is reported in [21]. Additionally, in [15], Kriging was employed to estimate shadowing only in indoor scenarios at 869.6 MHz, 1930.2 MHz, 2400 MHz and 2500 MHz without considering O2I non-line-of-sight (NLOS) links. Unlike the links studied in [15], in [6], the authors considered O2I NLOS; nevertheless, they only focused their study on comparing the performance of standard model predictions. To the best of our knowledge, this is the first time such novel modelling for estimating shadowing through Kriging in O2I links at 3.5 GHz for 5G communication systems has been presented.