Indoor‐to‐outdoor empirical path loss modelling for femtocell networks at 0.9, 2, 2.5 and 3.5 GHz using singular value decomposition

Allen, Ben; Mahato, Shyam Babu; Gao, Yuteng; Salous, Sana

doi:10.1049/iet-map.2016.0416

Cited by 8 publications

(37 citation statements)

References 10 publications

(33 reference statements)

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“…SVD pathloss model calibration introduced by [7] may be generalized through a specification of the nominal model to be calibrated according to (6) which is similar to Pm of (1), with the exception that the leading 'basis' function is now, by definition, [7], [8], set equal to unity. A 'design matrix' is then prescribed from the component parts of (6) according to (7) and the unknown calibration coefficients are then given by [7] (…”

Section: B Singular Value Decomposition Calibrationmentioning

confidence: 99%

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

Muhammed

Ayorinde

Okewole

et al. 2022

JCIS

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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: B Singular Value Decomposition Calibrationmentioning

confidence: 99%

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

show abstract

“…For the latter, ray-tracing was employed due to the challenges of performing extensive measurements at a statistically large number of different residential houses. By using complementary approaches in [97], Allen et al did not focus their work on characterizing the channel but on facilitating femtocell network deployment through two empirical I2O path loss models derived from continuous-wave (CW) power measurements. When field prediction for indoor base stations or access points is needed both inside and around the building, e.g., for interference assessment and for fingerprinting localization purposes, the model proposed by Degli et al in [98] can deliver predictions with good accuracy, based on a combination of a two-parameters propagation formula and a multi-wall model.…”

Section: ) Indoor-to-outdoor (I2o) Modelsmentioning

confidence: 99%

Bringing It Indoors: A Review of Narrowband Radio Propagation Modeling for Enclosed Spaces

2020

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Small cells are now widely deployed indoors to address hot-spot areas where capacity uplift is needed. This deployment leads to the increase of wireless networks as a challenge to service demands of personal communication systems, which has inspired the scientific community to work towards understanding and predicting in-building radio wave propagation performance. Despite this, only a few reviews have attempted to overview channel modeling for specific indoor environments and even fewer outline remarks that include a methodology for designing and planning indoor radio systems. Consequently, a comprehensive survey of indoor narrowband channel models is presented, spanning more than 30 years of continuous research to overview and contrast significant developments including their disadvantages, and proposing a new taxonomy to analyze them. Finally, remarks on indoor radio propagation modeling with a vision for future research opportunities are presented. ⋮ INDEX TERMS Indoor channel models, indoor radio wave propagation, wireless propagation.

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“…Two notable modelling techniques for indoor-tooutdoor scenarios are the Trust Region Algorithm (TRA) utilized by [9], and the Singular Value Decomposition (SVD) approach introduced by [10] for the development of pathloss models concerning femtocell networks in residential buildings. This paper's interest is in the SVD approach, with which [10] calibrated two different base pathloss models, to obtain significantly better results than reported by [9]. In particular, the paper first compares the performances of the SVD models presented by [10] with corresponding models developed with the QMM [11] calibration of basic ECC33 and WINNER II models.…”

Section: Introductionmentioning

confidence: 99%

“…This paper's interest is in the SVD approach, with which [10] calibrated two different base pathloss models, to obtain significantly better results than reported by [9]. In particular, the paper first compares the performances of the SVD models presented by [10] with corresponding models developed with the QMM [11] calibration of basic ECC33 and WINNER II models. The results of the comparisons clearly revealed that across all the four frequencies considered over the four sites treated by [10], the QMM-calibrated models had better MPE and RMSPE metrics than the two SVD models.…”

Section: Introductionmentioning

confidence: 99%

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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Indoor‐to‐outdoor empirical path loss modelling for femtocell networks at 0.9, 2, 2.5 and 3.5 GHz using singular value decomposition

Cited by 8 publications

References 10 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

Bringing It Indoors: A Review of Narrowband Radio Propagation Modeling for Enclosed Spaces

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

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