Statistical tuning of cost 231 Hata model in deployed 1800mhz GSM networks for a rural environment

Omoze, E.L.; Edeko, F.O.

doi:10.4314/njt.v39i4.30

Cited by 6 publications

(21 citation statements)

References 8 publications

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“…Computational (MPE, RMSE) results obtained for the QMM-calibrated COST231-Hata (rural area) emerged as (-0.22dB, 3.80dB), for 'Network A', (0.30dB, 2.67dB), for 'Network B', and (0.77dB, 3.06dB), for 'Network C'. Although these metrics are clearly much better than those recorded by the optimum model of [7], even better metrics were obtained for the corresponding QMM-calibrated ECC-33 models. For example, in the case of the ECC-33 (medium city model), (MPE, RMSE) values were obtained as (0.19dB, 1.19dB), (0.03dB, 2.04dB), and (0.12dB, 2.34dB), for networks 'A', 'B', and 'C', respectively.…”

Section: Introductionmentioning

confidence: 70%

“…RMSE metrics reported in the paper ranged between 6.717dB and 7.244dB, and may be regarded as consistent and satisfactory. Of particular interest to this paper is a recent publication by Omoze and Edeko [7], in which a least square approach, similar to that described in [2] was utilized for the tuning of the basic COST231-Hata model for rural areas, with field measurements taken from three GSM1800 networks in the NIFOR (Benin City) environment. Computational results (in dB) reported by the authors for (MPE, RMSE) were given as (1.17, 5.58) for 'Network A', (2.20, 7.16), for 'Network B', and (6.21, 10.78), for 'Network C'.…”

Section: Introductionmentioning

confidence: 99%

“…Computational results (in dB) reported by the authors for (MPE, RMSE) were given as (1.17, 5.58) for 'Network A', (2.20, 7.16), for 'Network B', and (6.21, 10.78), for 'Network C'. This paper utilizes the same measurement data reported in [7] for the QMM-calibration of five different basic models, including the COST231-Hata model for rural areas, considered by the authors of [7]. Computational (MPE, RMSE) results obtained for the QMM-calibrated COST231-Hata (rural area) emerged as (-0.22dB, 3.80dB), for 'Network A', (0.30dB, 2.67dB), for 'Network B', and (0.77dB, 3.06dB), for 'Network C'.…”

Section: Introductionmentioning

confidence: 99%

“…Thereafter, the generic process is specialized for use with basic pathloss model calibration, using the basic models utilized in the paper as candidate examples. In section 3, the QMMmodel-calibration process is validated through a comparative evaluation of its performance with the optimized model reported in [7], for the NI-FOR environment in Benin City. Further model evaluation is provided in the paper's section 4, where outcomes of calibration with measurement data obtained by the authors for other areas of Benin City, are comprehensively discussed.…”

Section: Introductionmentioning

confidence: 99%

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

Adelabu¹,

Ayorinde²,

Muhammed³

et al. 2021

Nig. J. Tech.

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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.

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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“…The Hata model is a classic model of signal link budget in cities. The COST-231 Hata model is an extended version of the Hata model, with an application frequency of 1500 2000 MHz [7]. The definition of Cost 231-Hata is as follows:…”

Section: Cost 231-hatamentioning

confidence: 99%

Dubhe: a deep-learning-based B5G coverage analysis method

Xiao

2021

EURASIP J. Adv. Signal Process.

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In recent years, with the rapid development of various technologies such as the Internet of Things and the Internet, the demand for massive device connections and a variety of differentiated new business applications has continued to increase. In order to better cope with the rapid growth of mobile data in the future, 5G also came into being. Then, B5G was proposed and applied in industries such as traditional voice/video, smart city, automotive car or ship, unmanned aerial vehicle, marine monitoring, IoT, and intelligent industry. In these scenarios, B5G is required to achieve seamless global coverage. As these scenarios are complex and changeable, analysis of the coverage of 5G base stations has become a challenge. We decompose the environment around the base station into multiple grids, and analyze the signal strength of each grid. A signal propagation model needs to be constructed to predict whether each grid is covered. The commonly used wireless propagation model is an empirical model based on a mathematical formula for statistical analysis of a large amount of test data during the establishment of a 5G local area network. It has universal applicability, but has insufficient prediction accuracy for specific scenarios. Therefore, it is necessary to calibrate and modify the typical propagation model according to the specific environment to obtain an accurate propagation model that matches the current area. We improved the traditional wireless communication model, and proposed a deep-learning-based B5G coverage analysis method named Dubhe which is one of the planets of the Big Dipper. In a real cell scenario, the mean square error of the link budget of the typical UMa model is 17.9 dBm, while the mean square error of the proposed Dubhe model constructed in this article is only 6.78 dBm. The recognition rate of weak coverage can reach 42.86%.

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Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network

2022

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There is an increased exploration of the potential of wireless communication networks in the automation of daily human tasks via the Internet of Things. Such implementations are only possible with the proper design of networks. Path loss prediction is a key factor in the design of networks with parameters such as cell radius, antenna heights, and the number of cell sites that can be set. As path loss is affected by the environment, satellite images of network locations are used in developing path loss prediction models such that environmental effects are captured. We developed a path loss model based on the Extreme Gradient Boosting (XGBoost) algorithm, whose inputs are numeric (non-image) features that influence path loss and features extracted from images composed of four tiled satellite images of points along the transmitter to receiver path. The model can predict path loss for multiple frequencies, antenna heights, and environments such that it can be incorporated into Radio Planning Tools. Various feature extraction methods that included CNN and hand-crafted and their combinations were applied to the images in order to determine the best input features, which, when combined with non-image features, will result in the best XGBoost model. Although hand-crafted features have the advantage of not requiring a large volume of data as no training is involved in them, they failed in this application as their use led to a reduction in accuracy. However, the best model was obtained when image features extracted using CNN and GLCM were combined with the non-image features, resulting in an RMSE improvement of 9.4272% against a model with non-image features only without satellite images. The XGBoost model performed better than Random Forest (RF), Extreme Learning Trees (ET), Gradient Boosting, and K Nearest Neighbor (KNN) based on the combination of CNN, GLCM, and non-image features. Further analysis using the Shapley Additive Explanations (SHAP) revealed that features extracted from the satellite images using CNN had the highest contribution toward the XGBoost model’s output. The variation in values of features with output path loss values was presented using SHAP summary plots. Interactions were also observed between some features based on their dependence plots from the computed SHAP values. This information, when further explored, could serve as the basis for the development of an explainable/glass box path loss model.

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Statistical tuning of cost 231 Hata model in deployed 1800mhz GSM networks for a rural environment

Cited by 6 publications

References 8 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

Dubhe: a deep-learning-based B5G coverage analysis method

Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network

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