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.
Small cells are now widely deployed indoors to address hot-spot areas where capacity uplift is needed. This deployment leads to the increase of wireless networks as a challenge to service demands of personal communication systems, which has inspired the scientific community to work towards understanding and predicting in-building radio wave propagation performance. Despite this, only a few reviews have attempted to overview channel modeling for specific indoor environments and even fewer outline remarks that include a methodology for designing and planning indoor radio systems. Consequently, a comprehensive survey of indoor narrowband channel models is presented, spanning more than 30 years of continuous research to overview and contrast significant developments including their disadvantages, and proposing a new taxonomy to analyze them. Finally, remarks on indoor radio propagation modeling with a vision for future research opportunities are presented. ⋮ INDEX TERMS Indoor channel models, indoor radio wave propagation, wireless propagation.
This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median path loss modeling and then includes ordinary Kriging to interpolate shadowing. The performance of this method is evaluated by investigating the spatial structure that follows shadowing through the semivariogram, covariance function, and correlogram as variography tools. It is shown that semivariogram outperforms the other statistics to describe shadowing spatial continuity in path loss modeling in terms of the mean absolute error.
How do you know you select enough tuning dataset from measurements to guarantee model prediction accuracy? Tuning datasets are often selected based on simple random sampling with predefined rates. Usually, these rates are determined as a/b, where a% of the data goes to training, and the remaining b% goes to testing. But it is not clear to what extent tuning dataset in order to minimize the estimation path loss errors. It is thus required to analyze the performance of channel modeling by selecting-among all measurement samples-appropriate tuning dataset. Using radio measurements and deterministic Ray Launching techniques to collect enough reliable samples, this study analyzes the impact of tuning dataset selection-expressed in terms of the mean absolute error and cost-on a novel Krigingaided in-building measurement-based path loss prediction model.
Deep knowledge of how radio waves behave in a practical wireless channel is required for the effective planning and deployment of radio access networks in outdoor-to-indoor (O2I) environments. Using more than 400 non-line-of-sight (NLOS) radio measurements at 3.5 GHz, this study analyzes and validates a novel O2I measurement-based path loss prediction narrowband model that characterizes and estimates shadowing through Kriging techniques. The prediction results of the developed model are compared with those of the most traditional assumption of slow fading as a random variable: COST231, WINNER+, ITU-R, 3GPP urban microcell O2I models and field measured data. The results showed and guaranteed that the predicted path loss accuracy, expressed in terms of the mean error, standard deviation and root mean square error (RMSE) was significantly better with the proposed model; it considerably decreased the average error for both scenarios under evaluation.
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