The performance of in‐building measurement‐based path loss modelling using kriging

Abstract

An accuracy evaluation analysis of a novel in-building measurement-based path loss prediction narrowband model is presented here, comparing the performance of Krigingaided shadowing prediction against the most traditional assumption of slow fading as a random variable and a classical estimation derived from linear interpolation. Extensive radio measurements were employed using distinct samples to calibrate (tuning dataset) and validate (testing dataset) the model. Path loss predictions are made over the testing dataset locations to compare it against the measured values, thus obtaining an error in the prediction from the difference between measurements and predictions. The results in the seven buildings evaluated show the potential of Kriging-aided channel modelling with a higher level of confidence than other modelling approaches compared hereafter.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Tuning Selection Impact on Kriging-Aided In-Building Path Loss Modeling

“…This percentage varies from 10% to 100% in order to validate the optimal quantity to consider in model tuning. This percentage is extracted according to the method validated in [10], which limits samples to classification zones defined by concentric circles every 5 m from the position of the transmitting antenna, and the corresponding a% is extracted from each classification zone. 3) Through the methodology described in Section II, the estimated path loss is calculated at each RL grid location.…”

“…To quantify and describe the spatial variability Kriging preliminary employs the variogram as part of the variography process, afterward, Kriging interpolates the shadowing values at a random location 𝑥 & from 𝑦 to obtain unknown shadowing samples according to the variogram outcomes. Then, as a result of this postprocessing, a shadowing grid is generated for the target area, for a better understanding, this process is described in detail in [10]. Finally, the estimated path loss process is performed and is calculated as follows…”

“…In most studies, these datasets are selected from the corresponding measurements according to a desired ratio a/b that is based on random sampling [8], [9], i.e., an a% for tuning the model and a b% for testing the model. In [10] the authors analyze the following approaches: what happens when the method selection of tuning dataset varies in order to select the right one to get the The authors wish to acknowledge the support received from the National Council of Science and Technology CONACYT, through the student scholarship number 746015.…”

mmWave Channel Measurements for 3-D Path Loss Analysis and Model Design in Stadiums

“…where N(h) is the number of pairs of measured locations within the lag interval h selected to estimate the path loss at an unmeasured point with c0 3D coordinates, in other words, h is the size of a distance class into which pairs of locations N(h) are grouped. An extensive mathematical description of the variography and ordinary Kriging process is described in [14].…”

“…In addition, and towards decrease the quantity of required samples to train Kriging, the 20 -120 samples were extracted with a percentage varying from 10 -60 percent. The boundary of 60 percent was selected according to the findings reported in [14]. The datasets are selected according to the three cases described in Section III, e.g., for case 1 in Table II, if 60 percent is selected to extract samples, it is ensured that this percentage is randomly-extracted from each zone of the first case (illustrated in Fig.…”