Prediction of Call Drops in GSM Network using Artificial Neural Network - Olaonipekun Oluwafemi Erunkulu; Elizabeth Nnonye Onwuka; Okechukwu Ugweje; Lukman Adewale Ajao (Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro, 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.