On the Cross-Application of Calibrated Pathloss Models Using Area Features: Finding a way to determine similarity between areas

Zhang, Jiayi; Gentile, Camillo; Garey, Wesley

doi:10.1109/map.2019.2943272

Cited by 9 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, it is apparent that the QMM, as comprehensively described and utilized in this paper, represents an excellent candidate for investigations concerning the 'cross-application' of pathloss prediction models, as proposed by Zhang et al [17].…”

Section: Further Model Performance Eval-uationmentioning

confidence: 95%

An evaluation of Quasi-Moment-Method calibrated pathloss models for Benin City Nigeria

Adelabu¹,

Ayorinde²,

Muhammed³

et al. 2021

Nig. J. Tech.

View full text Add to dashboard Cite

This paper introduces the Quasi-Moment-Method (QMM) as a novel radiowave propagation pathloss model calibration tool, and evaluates its performance, using field measurement data from different cellular mobile communication network sites in Benin City, Nigeria. The QMM recognizes the suitability of component parameters of existing basic models for the definition of ‘expansion’ and ‘testing functions’ in a Galerkin approach, and simulations were carried out with the use of a FORTRAN program developed by the authors, supported by matrix inversion in the MATLAB environment. Computational results reveal that in terms of both Root Mean Square (RMS) and Mean Prediction (MP) errors, QMM-calibrated models performed much better than an ‘optimum’ model reported for the NIFOR (Benin City), by a recent publication. As a matter of fact, the QMM-calibrated COST231 (rural area) model recorded reductions in RMS error of between 31.5% and 71% compared with corresponding metrics due to the aforementioned ‘optimum’ model. The simulation results also revealed that of the five basic models (COST231-rural area and suburban city, ECC33 (medium and large sized cities), and Ericsson models) utilized as candidates, the two ECC33 models, whose performances were consistently comparable, represented the best models for QMM-model calibration in the Benin City environments investigated.

show abstract

Section: Further Model Performance Eval-uationmentioning

confidence: 95%

An evaluation of Quasi-Moment-Method calibrated pathloss models for Benin City Nigeria

Adelabu¹,

Ayorinde²,

Muhammed³

et al. 2021

Nig. J. Tech.

View full text Add to dashboard Cite

show abstract

“…Let the generic nominal pathloss prediction model be described by (1) in which may, in general, be functions of separation of transmitter and receiver (d), frequency (f), transmitter antenna height (hte), receiver antenna height (hre), constants, or some combinations of them. The QMM algorithm determines a set of 'N' coefficients, such that at every measurement point dk,…”

Section: A the Quasi-moment-methodsmentioning

confidence: 99%

“…Approaches, which calibrate nominal prediction models in the sense of calibration as defined by [1] include the Cuckoo-search optimization algorithm presented in [6]. In this case, four parameters of a 'UFPA' model, developed for a 5.8GHz network were calibrated to specialize the model for use in a 2.6GHz network.…”

Section: Introductionmentioning

confidence: 99%

“…PARTICULARLY useful description of propagation pathloss model calibration is that given in [1] as the process of using measurement information to fine-tune nominal models towards improving the models' prediction performances. A number of pathloss model calibration routines have been reported in the literature, the more popular of them being the Least Square Error (LSE) and the Minimum Mean Square Error (MMSE) approaches described in [2] and [3], respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Muhammed

Ayorinde

Okewole

et al. 2022

JCIS

View full text Add to dashboard Cite

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.

show abstract

“…Thus, applying the models out of their frequency range, antenna height, and terrain specification usually results in significant errors [20], [22], [23]. In [24], the authors used terrain-based field signal measurement techniques to conduct a thorough propagation path loss calibration for rural, urban and suburban CDMA and LTE as case studies in the USA. The researchers realized between 4.5 dB to 8 dB performance accuracies in terms of RMSE error for the case studied environment compared to the usual prediction models that achieved between 7.4 to 11 dB performance accuracy.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Tuning of the Log-distance Model for Optimal PredictiveModeling of Pathloss Over Irregular Terrains

E. Olukanni,

Isabona,

Odesanya

et al. 2023

IJCDS

View full text Add to dashboard Cite

Recent technological development has facilitated the deployment of different Mobile Broadband Cellular Network Systems (MBCNS), such as Long Term Evolution (LTE) and 5G New Radio (NR), globally. This development aims at satisfying the ever-data-hungry multimedia applications to guarantee good quality of service for mobile subscribers. One way mobile subscribers can continuously access and enjoy the services provided by the MBCNS is to put in place a well-planned signal coverage area, wherein accurate path loss estimation is prioritized. Particularly, with the regular technological advancements and evolution of mobile communication systems, particularly fourth and fifth, the development of accurate précised path loss models has become more critical for robust planning and optimization purposes. In practice, the conventional log-distance path loss model is suitable for path loss predictive modeling and estimation in plain signal propagation environments. However, the model is not suitable for irregular terrains. In order to adapt this model for application in irregular terrains, this paper proposes a modified logdistance path loss model with an adaptive polynomial term. The modified long-distance path loss model provides efficient irregular terrain signal loss estimation parameters. In order to boost the prediction accuracy of the proposed model, two regression optimization methods, non-linear least square and weighted least square, were employed with the Levenberg-Marquardt (LM) algorithm to determine its relevant parameters. In terms of the coefficient of correlation and percentage error, the modified logdistance path loss model with the optimized parameters showed 40-60% improvement in accuracy over the standard log-distance model for the path loss prediction across the six different locations investigated. Furthermore, the validation of the proposed model has been provided in order to ascertain the level of its prediction accuracies in other locations. Overall, the modified log-distance model showed remarkable accuracy and efficiency when deployed in a related wireless propagation environment.

show abstract

On the Cross-Application of Calibrated Pathloss Models Using Area Features: Finding a way to determine similarity between areas

Cited by 9 publications

References 24 publications

An evaluation of Quasi-Moment-Method calibrated pathloss models for Benin City Nigeria

An evaluation of Quasi-Moment-Method calibrated pathloss models for Benin City Nigeria

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

Adaptive Tuning of the Log-distance Model for Optimal PredictiveModeling of Pathloss Over Irregular Terrains

Contact Info

Product

Resources

About