Radio Frequency Optimization of Mobile Networks in Abeokuta, Nigeria for Improved Quality of Service

Oseni, Olasunkanmi F.

doi:10.15623/ijret.2014.0308027

Cited by 21 publications

References 2 publications

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An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Ojo

Akkaya

Sopuru

2022

Int J Communication

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Summary Accurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.

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An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Ojo

Akkaya

Sopuru

2022

Int J Communication

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Artificial Neural Network Model for Path Loss Predictions in the VHF Band

Popoola

Фарук

Surajudeen-Bakinde

et al. 2021

Conference Proceedings of ICDLAIR2019

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Data on the key performance indicators for quality of service of GSM networks in Nigeria

et al. 2018

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In this data article, the Key Performance Indicators (KPIs) for Quality of Service (QoS) of Global System for Mobile Communications (GSM) networks in Nigeria are provided and analyzed. The data provided in this paper contain the Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Stand-alone Dedicated Channel (SDCCH) congestion, and Traffic Channel (TCH) congestion for the four GSM network operators in Nigeria (Airtel, Etisalat, Glo, and MTN). These comprehensive data were obtained from the Nigerian Communications Commission (NCC). Significant differences in each of the KPIs for the four quarters of each year were presented based on Analysis of Variance (ANOVA). The values of the KPIs were plotted against the months of the year for better visualization and understanding of data trends across the four quarters. Multiple comparisons of the mean-quarterly differences of the KPIs were also presented using Tukey's Post Hoc test. Public availability and further interpretation and discussion of these useful information will assist the network providers, Nigerian government, local and international regulatory bodies, policy makers, and other stakeholders in ensuring access of people, machines, and things to high quality telecommunications services.

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Radio Frequency Optimization of Mobile Networks in Abeokuta, Nigeria for Improved Quality of Service

Abstract: Radio Frequency Optimization is an important process in verifying and

Cited by 21 publications

References 2 publications

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Artificial Neural Network Model for Path Loss Predictions in the VHF Band

Data on the key performance indicators for quality of service of GSM networks in Nigeria

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