Prediction of Call Drops in GSM Network using Artificial Neural Network

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)
Elizabeth Nnonye Onwuka (Department of Telecommunications Engineering, Federal University of Technology Minna Main Campus, Gidan Kwanu, Along Minna - Bida Road PMB 65 Minna, Niger State, Nigeria)
Okechukwu Ugweje (Department of Electrical and Electronics Engineering, Nigerian Turkish Nile University State Cadastral Zone, Plot 681 Airport Rd, Jabi, Abuja, Nigeria)
Lukman Adewale Ajao (Department of Computer Engineering, Federal University of Technology Minna Main Campus, Gidan Kwanu, Along Minna - Bida Road PMB 65 Minna, Niger State, Nigeria)

Jurnal Teknologi dan Sistem Komputer
Volume 7, Issue 1, Year 2019 (January 2019)

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.