A Novel Propagation Pathloss Model Calibration Tool

Ayorinde, Ayotunde; Muhammed, Hisham Abubakar; Okewole, Francis Olutunji; Mowete, A. Ike

doi:10.15866/irecap.v11i3.19796

Cited by 3 publications

(5 citation statements)

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“…The prediction performances of the calibrated models were evaluated with the use of the common performance metrics of Mean Prediction Error (MPE), and for the purposes of assessing the uniqueness of solution, Root Mean Square Error (RMSE) and Grey Relational Grade-Mean Absolute Percentage Error (GRG-MAPE). The MPE metrics were in all cases, as impressive as those reported elsewhere, [9], [15], and the RMSE and GRG-MAPE metrics very clearly indicate that the QMM solution to the pathloss model calibration problem is indeed unique, when the conditions specified in [9] and [15] are satisfied. For example, the difference between the largest and smallest recorded RMSE values for the models calibrated with measurements from the 2.6GHz network (Table I) is 0.5038dB, with 0.7415dB and 0.0331 as the corresponding values for the 1800MHz (Table II) and 26GHz (Table III), respectively.…”

Section: The Grey Relational Grade Mape Algorithmsupporting

confidence: 76%

“…The biggest difference of 0.0691dB in RMSE between a QMM-calibrated nominal model and its corresponding alternative model was in this case, recorded by ITU case. The virtually identical RMSE values recorded by all the calibrated models of the three examples considered in this paper very clearly support the uniqueness property of the QMM pathloss model calibration algorithm as defined in [9], [15]. It has however been suggested, [6], [13], [14], that a better assessment of pathloss model prediction performance is offered by the Grey Relational Grade Mean Absolute Percentage Error, GRG-MAPE.…”

Section: Fig 3 Comparison Of Pathloss Predicted By Calibrated Modelssupporting

confidence: 69%

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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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Section: The Grey Relational Grade Mape Algorithmsupporting

confidence: 76%

Section: Fig 3 Comparison Of Pathloss Predicted By Calibrated Modelssupporting

confidence: 69%

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

Muhammed

Ayorinde

Okewole

et al. 2022

JCIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…(2) is that ( [11]; [13]) the Euclidean semi-norm of the error function should assume a minimum value; that is (6) should be minimum. Also, in this case, the calibration coefficients are determined through the definition of a 'model calibration matrix' according to [14] (7)…”

Section: 2mentioning

confidence: 99%

“…In addition to the QMM-calibration of the base models of Eqns. ( 13), (14), and ( 15), the SVD models reported by [10] as deriving from Eqns. (11) and (12) were also subjected to QMM-calibration.…”

Section: 2mentioning

confidence: 99%

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Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Muhammed¹,

Ayorinde²,

Okewole³

et al. 2022

Nig. J. Tech.

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Using indoor-to-outdoor pathloss measurements for a femtocell network, this paper presents a comparative evaluation of the performances of Singular Value Decomposition (SVD) and Quasi-Moment-Method (QMM) as pathloss model calibration tools. First, the performances of two published SVD models are compared with those of corresponding QMM models, developed through the calibration of basic ECC33 and WINNER II models. Then, and after noting that the ‘base models’ from which the poorer performing, published SVD calibrations reportedly derive, are either incompletely described or characterized by misprints, alternative ‘base models’ are prescribed by this paper. It is then shown through analysis that QMM and SVD represent alternative implementations of the same basic model calibration algorithm. Computational results due to the alternative models suggest that better performance metrics (Mean Prediction Error (MPE) and Root Mean Square Prediction Error (RMSPE)) are recorded, when existing basic models are modified to mimic the SVD ‘base model’, prior to SVD/QMM calibration. Indeed, because the MPE due to the calibration of the alternative models are all close to zero (actually equal to zero in a few cases), the associated residual profiles closely follow the Gaussian distribution typically assumed in the literature, for shadow fading modelling.

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Performance Evaluation of LoRa 915 MHz for IoT Communication System on Indonesian Railway Tracks with Environmental Factor Propagation Analysis

Suharjono,

Mukhlisin,

Wardihani

et al. 2023

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

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A Novel Propagation Pathloss Model Calibration Tool

Cited by 3 publications

References 0 publications

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

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

Singular Value Decomposition and Quasi-Moment-Method as pathloss model calibration alternatives

Performance Evaluation of LoRa 915 MHz for IoT Communication System on Indonesian Railway Tracks with Environmental Factor Propagation Analysis

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