A Quasi-Moment-Method empirical modelling for pathloss prediction

Adekola, S. Adeniyi; Ayorinde, Ayotunde; Okewole, Francis; Mowete, A. Ike

doi:10.1080/21681724.2021.1908607

Cited by 4 publications

(20 citation statements)

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“…In a pair of companion papers, Popoola et al [14,15] presented results of the performance evaluation of five pathloss prediction models, using the results of extensive field measurements carried out over three routes at the campus of the Covenant University, Ota, Nigeria. Although like many similar publications, the comparisons reported in [11,14] suggested that the basic Okumura-Hata and the COST231-Hata models provide the best results for the campus environments investigated, it has since been demonstrated [16][17][18], that when basic models are calibrated by the recently introduced Quasi-Moment-Method [16], the calibrated ECC-33 models provide the best prediction metrics.…”

Section: Introductionmentioning

confidence: 86%

“…assumes its lowest possible value, [16][17][18]. The calibration coefficients n κ appearing in (2) derive directly from the matrix operations defined by…”

Section: The Quasi-moment-methodsmentioning

confidence: 99%

“…in which the calibration coefficients are the entries into the column vector ( ) Κ , and where the mn th element of the model calibration matrix is given by, [16],…”

Section: The Quasi-moment-methodsmentioning

confidence: 99%

“…As was noted in [16], the real, symmetric model calibration matrices of the QMM calibration process are typically characterized by one predominant eigenvalue. In this section, the features of the eigenvalues associated with the nine basic pathloss prediction models are, for each of routes A, B, and C, further explored, with reference to the influence of size of measurement data.…”

Section: Eigenvalues Of the Model Calibration Matrixmentioning

confidence: 99%

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On Some Properties of the Quasi-Moment-Method Pathloss Model Calibration

Ayotunde¹,

Michael²,

Hisham³

et al. 2021

JEEE

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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.

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Section: Introductionmentioning

confidence: 86%

“…assumes its lowest possible value, [16][17][18]. The calibration coefficients n κ appearing in (2) derive directly from the matrix operations defined by…”

Section: The Quasi-moment-methodsmentioning

confidence: 99%

“…in which the calibration coefficients are the entries into the column vector ( ) Κ , and where the mn th element of the model calibration matrix is given by, [16],…”

Section: The Quasi-moment-methodsmentioning

confidence: 99%

Section: Eigenvalues Of the Model Calibration Matrixmentioning

confidence: 99%

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On Some Properties of the Quasi-Moment-Method Pathloss Model Calibration

Ayotunde¹,

Michael²,

Hisham³

et al. 2021

JEEE

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show abstract

“…Essentially, the method minimizes mean square error in a calibration process similar to the SVD regression described in [8], and involving the singular value decomposition of a 'design matrix', whose entries are the parameters of the nominal model to be subjected to calibration. Unlike the algorithms of [6] and [7], the Quasi-Moment-Method (QMM) recently introduced by [9], has been shown to be able to calibrate all existing nominal models for the prediction of pathloss in both indoor and outdoor environments. One important property ascribed to the QMM by [9] is that when the 'basis' functions are linearly independent, the QMM solution is unique.…”

Section: Introductionmentioning

confidence: 99%

On the Uniqueness of the Quasi-Moment-Method Solution to the Pathloss Model Calibration Problem

Muhammed

Ayorinde

Okewole

et al. 2022

JCIS

Self Cite

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Investigations in this paper focus on establishing the uniqueness properties of the Quasi-Moment-Method (QMM) solution to the problem of calibrating nominal radiowave propagation pathloss prediction models. Nominal (basic) prediction models utilized for the investigations, were first subjected to QMM calibrations with measurements from three different propagation scenarios. Then, the nominal models were recast in forms suitable for Singular Value Decomposition (SVD) calibration before being calibrated with both the SVD and QMM algorithms. The prediction performances of the calibrated models as evaluated in terms of Root Mean Square Prediction Error (RMSE), Mean Prediction Error (MPE), and Grey Relational Grade-Mean Absolute Percentage Error (GRG-MAPE) very clearly indicate that the uniqueness of QMMcalibrations of basic pathloss models is more readily observable, when the basic models are recast in forms specific to SVD calibration. In the representative case of calibration with indoorto-outdoor measurements, RMSE values were recorded for QMM-calibrated nominal models as 5.2639dB for the ECC33 model, and 5.3218dB for the other nominal models.Corresponding metrics for the alternative (rearranged) nominal models emerged as 5.2663dB for the ECC33 model and 5.2591dB for the other models. A similar general trend featured in the GRG-MAPE metrics, which for both SVD and QMM calibrations of all the alternative models, was recorded as 0.9131, but differed slightly (between 0.9138 and 0.9196) for the QMM calibration of the nominal models. The slight differences between these metrics (due to computational round-off approximations) confirm that when the components of basic models are linearly independent, the QMM solution is unique. Planning for wireless communications network deployment may consequently select any basic model of choice for QMM-calibration, and hence, identify relative contributions to pathloss by the model's component parts.

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On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

Adekola,

Ayorinde,

Muhammed

et al. 2023

JCIS

Self Cite

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A computationally inexpensive, analytically simple, and remarkably efficient rain attenuation prediction algorithm is presented in this paper. The algorithm, here referred to as the Quasi Moment-Method (QMM), has only two main requirements for its implementation. First, rain attenuation measurement data (terrestrial or slant path) for the site of interest must be available; and second, a model, known to have predicted attenuation for any site to a reasonable level of accuracy (base model), and whose analytical format can be expressed as a linear combination of its parameters, is also required. An important novelty introduced by the QMM algorithm is a normalization scheme, through which a modelling difficulty concerning exceedance probabilities outside a 0.01 to 1.00 range, is eliminated. Model validation and performance evaluation using a comprehensive set of data available from the literature clearly demonstrated that the QMM models consistently improved base model performance by more than 90%; and outperformed all published ‘best fit’ models with which they were compared.

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A Quasi-Moment-Method empirical modelling for pathloss prediction

Cited by 4 publications

References 11 publications

On Some Properties of the Quasi-Moment-Method Pathloss Model Calibration

On Some Properties of the Quasi-Moment-Method Pathloss Model Calibration

On the Uniqueness of the Quasi-Moment-Method Solution to the Pathloss Model Calibration Problem

On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

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