Abstract
Certain properties of the recently introduced Quasi-Moment-Method (QMM) for the calibration of basic radiowave propagation pathloss models are systematically examined in this paper. Using measurement data concerning three different routes located in a smart campus environment and made available in the open literature, the paper, in particular, investigates the effects of size of pathloss measurement data on the outcomes of the QMM calibration of nine basic pathloss models: namely, COST 231-urban and sub-urban cities models, ECC33-large and medium sized cities models, and the Egli, Ericsson, Hata, Lee, and SUI-‘Terrain A’ models. Computational results reveal that for the data sizes considered, and in the cases of the basic COST 231 and Hata models, which share identical correction factors for receiver antenna height, the ‘model calibration matrix’ becomes ill-conditioned for one choice of basis functions. The corresponding calibrated models, however, still predict pathloss with accuracy typical of the QMM. For example, Root Mean Square Error (RMSE) outcomes of predictions due to the calibration of these models, emerged as approximately the same for these three models; with values of 6.03 dB (Route A), 7.96 dB (Route B), and 6.19 dB (Route C). The results also show that when model calibration utilizes measurement data for distances further away from the transmitters (by ignoring measurement data for radial distances less than 100m away from the transmitters) significant improvements in RMSE metrics were recorded. The paper, in terms of the eigenvalues of the model calibration matrices, further examined the responses of these models to calibration with large-sized measurement data, to find that the model calibration matrices remained characterized, in each case, by a distinctly dominant eigenvalue. An important conclusion arising from the results of the investigations is that whereas the QMM model calibration process may lead, in some cases, and when large-sized measurement data is involved, to ‘badly-scaled’ model calibration matrices, the calibrated models still record very good assessment metrics. Computational results also reveal that with large-sized data sets, QMM models yield pathloss predictions with excellent (close to 0 dB) mean prediction errors.
Highlights
A few decades ago, Durgin, Rappaport and Xu, [1], predicted that with the advent in the USA, of the National Information Infrastructure (NII) systems, campus-wide wireless networks will proliferate in universities
Root Mean Square Error (RMSE) and Mean Prediction Error (MPE) metrics for these alternative models are presented in Table 4, from which it is readily observed that RMSE, for the ECC33 models, improved by about 43%, and by about 33% for the other alternative models
The results presented here reveal that the basic ECC33 is the best performing (7.5838dB-Route A; 8.0903dB-Route B; and 11.381dB-Route C) across all three measurement routes The results show that whereas the basic COST231 sub-urban city model had a better RMSE metric than the urban city model in the case of Route B, the latter’s RMSE metrics were better than the former’s for Route A (8.2309dB) and Route C (16.343dB)
Summary
A few decades ago, Durgin, Rappaport and Xu, [1], predicted that with the advent in the USA, of the National Information Infrastructure (NII) systems, campus-wide wireless networks will proliferate in universities. A similar study was reported by Nwawelu et al [4], whose investigations focused on a 2.4GHz network deployed at the Nsukka campus of the University of Nigeria In their contribution, Fraga-Lamas et al [5], utilized measurement results for a campus extending over an area of 26000m2, to evaluate the performance of a network simulator, implemented for a smart campus scenario. Some of the other investigations of interest to wireless networks deployed in university campuses either compared the performances of existing pathloss models or developed empirical models for the networks This category includes investigations conducted by Ogunjide et al [7], who took measurements at the Gidan Kwano Campus of the Federal University of Technology Minna, Nigeria. The paper reported outcomes of the performance evaluation of certain basic prediction models as well as empirical model developed with the use of the measurement data.
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