Propagation measurements and modelling at 1800 MHz in Lagos Nigeria

Ajose, S. O.; Imoize, Agbotiname Lucky

doi:10.1504/ijwmc.2013.054042

Cited by 45 publications

(42 citation statements)

References 11 publications

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“…Since this research is based on observations and measurements, empirical models will be considered, and some of the most frequently used basic empirical path loss model equations [9,14,[19][20][21] are as follows:…”

Section: Combiner and Filter Loss (Db)mentioning

confidence: 99%

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Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

Eichie¹,

Oyedum²,

Ajewole³

et al. 2017

PIER M

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Abstract-Propagation path loss models are useful for the prediction of received signal strength at a given distance from the transmitter; estimation of radio coverage areas of Base Transceiver Stations (BTS); frequency assignments; interference analysis; handover optimisation; and power level adjustments. Due to the differences in: environmental structures; local terrain profiles; and weather conditions, path loss prediction model for a given environment using any of the existing basic empirical models such as the Okumura-Hata's model has been shown to differ from the optimal empirical model appropriate for such an environment. In this paper, propagation parameters, such as distance between transmitting and receiving antennas, transmitting power and terrain elevation, using sea level as reference point, were used as inputs to Artificial Neural Network (ANN) for the development of an ANN based path loss model. Data were acquired in a drive test through selected rural and suburban routes in Minna and environs as dataset required for training ANN model. Multilayer perceptron (MLP) network parameters were varied during the performance evaluation process, and the weight and bias values of the best performed MLP network were extracted for the development of the ANN based path loss models for two different routes, namely rural and suburban routes. The performance of the developed ANN based path loss model was compared with some of the existing techniques and modified techniques. Using Root Mean Square Error (RMSE) obtained between the measured and the model outputs as a measure of performance, the newly developed ANN based path loss model performed better than the basic empirical path loss models considered such as: Hata; Egli; COST-231; Ericsson models and modified path loss approach.

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Section: Combiner and Filter Loss (Db)mentioning

confidence: 99%

“…To obtain a quantitative measure of the closeness of the predicted path loss to the measured path loss, [20] proposed the use of Root Mean Square Error (RMSE) given in Equation (14):…”

Section: Comparison Of Model Predicted Path Loss With Measured Path Lossmentioning

confidence: 99%

Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

Eichie¹,

Oyedum²,

Ajewole³

et al. 2017

PIER M

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show abstract

“…The RSS and spatial data d are captured in a suburban area for a 18000MHz GSM network. The RSS is converted to the measured pathloss (PL) using the formula [23][24][25]:…”

Section: Received Signal Strength (Rss) and Spatial Data Collection Amentioning

confidence: 99%

Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

Chimaobi¹,

Nnadi²,

Nkwocha³

2017

COM

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Abstract:Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.

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“…Past projects [5,6] have also suggested the Cost 231-Hata model to show the best performance in Lagos environments, hence its adoption as a reference model.…”

confidence: 99%

“…Sub-Urban [5] or path loss modeling of three different environs using just one frequency of modulation (1800MHz) [6] we therefore tried to take these studies further by considering four different G.S.M environments, modeled for these environments putting the 2G and 3G frequencies, i.e. 900MHz, 1800MHz, and 2100MHz into research consideration.…”

Section: Problem Statementmentioning

confidence: 99%

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2016

Review of Computer Engineering Research

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This paper presents path loss measurement and modeling for Lagos state dense-urban (DU), urban (UR), sub-urban (SU) and non-urban (NU) G.S.M environments. It was carried out with data collection through drive testing using TEMS Contribution/ OriginalityThis study is one of very few studies which have investigated path loss in classified environment. Effort was geared towards four chosen environment in Lagos metropolitan terrain. A new model known as OMODEEN was developed and can be used for specific environs. MATLAB was used to develop graphical representation of the model.

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Propagation measurements and modelling at 1800 MHz in Lagos Nigeria

Cited by 45 publications

References 11 publications

Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

Comparative Analysis of Basic Models and Artificial Neural Network Based Model for Path Loss Prediction

Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

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