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Olaonipekun Oluwafemi Erunkulu
Department of Computer Engineering, Federal University of Technology Minna Main Campus, Gidan Kwanu, Along Minna - Bida Road; PMB 65 Minna, Niger State, Nigeria
Computer Science & IT Electrical & Electronics Engineering

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Prediction of Call Drops in GSM Network using Artificial Neural Network Olaonipekun Oluwafemi Erunkulu; Elizabeth Nnonye Onwuka; Okechukwu Ugweje; Lukman Adewale Ajao
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 1, Year 2019 (January 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (518.381 KB) | DOI: 10.14710/jtsiskom.7.1.2019.38-46

Abstract

Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network.
Co-Authors Elizabeth Nnonye Onwuka Lukman Adewale Ajao Okechukwu Ugweje