Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment
| dc.contributor.author | Popoola SI | |
| dc.contributor.author | Atayero AA | |
| dc.contributor.author | Popoola OA | |
| dc.date.accessioned | 2022-07-26T12:18:25Z | |
| dc.date.available | 2022-07-26T12:18:25Z | |
| dc.date.issued | 2018 | |
| dc.description | Data in Brief | |
| dc.description.abstract | Empirical models are most widely used for path loss predictions because they are simple, easy to use, and require less computational efficiency when compared to deterministic models. A number of empirical path loss models have been developed for efficient radio network planning and optimization in different types of propagation environments. However, data that prove the suitability of these models for path loss predictions in a typical university campus propagation environment are yet to be reported in the literature. In this data article, empirical prediction models are comparatively assessed using the path loss data measured and predicted for a university campus environment. Field measurement campaigns are conducted at 1800 MHz radio frequency to log the actual path losses along three major routes within the campus of Covenant University, Nigeria. Path loss values are computed along the three measurement routes based on four popular empirical path loss models (Okumura-Hata, COST 231, ECC-33, and Egli). Datasets containing measured and predicted path loss values are presented in a spreadsheet file, which is attached to this data article as supplementary material. Path loss prediction data of the empirical models are compared to those of the measured path loss using first-order statistics, boxplot representations, tables, and graphs. In addition, correlation analysis, Analysis of Variance (ANOVA), and multiple comparison post-hoc tests are performed. The prediction accuracies of the empirical models are evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error Deviation (SED). In conclusion, the high-resolution path loss prediction datasets and the rich data exploration provided in this data article will help radio network engineers and academic researchers to determine the empirical model that is most suitable for path loss prediction in a typical university campus environment. | |
| dc.identifier.citation | 10.1016/j.dib.2018.03.040 | |
| dc.identifier.issn | 2352-3409 | |
| dc.identifier.uri | https://nerd.ethesis.ng/handle/123456789/340 | |
| dc.language.iso | en | |
| dc.subject | Models | |
| dc.subject | Forecasting | |
| dc.subject | Path loss | |
| dc.subject | Loss models | |
| dc.subject | Radio propagation | |
| dc.subject | Smart campus | |
| dc.title | Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment | |
| dc.type | Article |
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