Modeling and Development of a Novel Quality of Service Prediction Model for Global System for Mobile Communications Network using Artificial Neural Networks
Quality of service (QoS) performance evaluation is an essential indicator in determining the efficiency of services rendered by an industry. Comparison of some key performance indicators with standard threshold values has been a major approach for determining QoS of Global System for Mobile Communication (GSM) in Nigeria. This comparative approach, which usually involves human involvement is prone to error. Thus, an automatic artificial neural networks (ANN) predictive QoS model was developed in the study presented in this paper. In carrying out the study, five key performance indicators (KPIs) data were collected form the GSM operator used. The collected KPIs parameters were used to develop a mathematical model that was transformed into the proposed automatic QoS predicted model using ANN. The developed QoS prediction model when evaluated was found to be accuracy and performed favorably well when compared with the manual approach being used by the Nigerian Communications Commission. The developed automatic QoS prediction model for this study is thus suggested as a better replacement for the current manual method based on its accuracy and non-human involvement in predicting QoS of GSM network investigated.
Popoola, J.J., Megbowon, I.O. and Adeloye, V.S.A. (2009). Performance Evaluation and Improvement on Quality of Service of Global System for Mobile Communications in Nigeria, Journal of Information Technology Impact, Vol. 9, No. 2, 91-106.
Mebawondu, O.J., Dahunsi, F.M., Adewale, S.O. and Alese, B.K. (2018). Radio Access Evaluation of Cellular Network in Akure Metropolis, Nigeria, Nigerian Journal of Technology, Vol. 37, No. 3, 703-719.
Bakare, A.S. and Lola, G.K. (2011). Estimating the Impacts of Global System for Mobile Communication (GSM) on Income, Employment and Transaction Cost in Nigeria, Journal of Economics and International Finance, Vol. 3, No. 1, 37-45.
Okogun, O.A., Awoleye, O.M. and Siyanbola, F. (2012). Economic Value of ICT Investment in Nigeria: Is it Commensurate? International Journal of Economics and Management Sciences, Vol. 1, No. 10, 22-30.
Olukotun, G., Ademola, J., Olusegun, O. and Olorunfemi, K. (2013). The Introduction of GSM Services in Anyigba Community and its Impacts on Students Expenditure Pattern, Global Journal of Management and Business Research, Vol. 13, No. 8C, 73-81.
Chidozie, G., Lawal, P.O. and Ajayi, O.O. (2015). Deregulation of the Nigerian Telecommunication Sector: Interrogating the Nexus between Imperlism and Development, Academic Journal of Interdisciplinary Studies, Vol. 4, No. 1, 173-184.
Nkoreh, N., Bob-Manuel, I. and Olowononi, F. (2017). The Nigerian Telecommunication Industry: Analysis of the First Fifteen Years of the Growths and Challenges in the GSM Market (2001-2016), in Proceedings of the World Congress on Engineering and Computer Science 25-27 October, San Francisco, Vol. I, 1-5.
Ojo, O.J., Popoola, J.J., Oyetunji, S.A., Olasoji, Y.O. and Adu, M.R. (2019). Performance Evaluation of a Selected Cellular Mobile Operator in Ibadan Metropolis, Nigeria, Journal of Multidisciplinary Engineering Science and Technology, Vol. 6, No. 5, 10115-10124.
Galadanci, G.S.M. and Abdullahi, S.B. (2018). Performance Analysis of GSM Networks in Kano Metropolis of Nigeria, American Journal of Engineering Research, Vol. 7, No. 5, 69-79.
Kehinde, A.I., Lawal, S., Adunola, F.O. and Isaac, A.I. (2017). GSM Quality of Service Performance in Abuja, Nigeria, International Journal of Computer Science, Engineering and Applications, Vol. 7, No. ¾, 29-40.
Agubor, C.K., Chukwuchekwa, N.C., Atimati, E.E., Iwuchukwu, U.C. and Ononiwu, G.C. (2016). Network Performance and Quality of Service Evaluation of GSM Providers in Nigeria: A Case Study of Lagos State, International Journal of Engineering Sciences and Research Technology, Vol. 3, No. 9, 256-263.
Ozovehe, A. and Usman, A.U. (2015). Performance Analysis of GSM Networks in Minna Metropolis of Nigeria, Nigerian Journal of Technology, Vol. 34, No. 2, 359-367.
Akinyemi, L.A., Makanjuola, N.T., Shoewu, O.O. and Edeko, F.O. (2014). Evaluation and Analysis 3G Network in Lagos Metropolis, Nigeria, International Journal of Electrical and Computer Engineers System, Vol. 2, No, 3, 81-87.
Abayomi-Alli, A., Ezomo, P.I., Etuk, D.J., Oghogho, I. and Izilein, F. (2012). Performance Evaluation of GSM Service Providers around Igbinedion University Campuses, Advanced Materials Research, Vol. 367, 177-184.
Popoola, J.J. and van Olst, R. (2011). Automatic Classification of Combined Analog and Digital Modulation Schemes using Feedforward Neural Network, in Proceedings of IEEE AFRICON, Livingstone, Zambia, 13-15 September, doi:101109/AFRICON.2011.607208
Stauba, S., Karamanb, E., Kayaa, S., KarapÕnara, H. and Güvena, E. (2015). Artificial Neural Network and Agility, Procedia - Social and Behavioral Sciences, Vol. 195, 1477 - 1485.
Javed, A.K. and Irfan, Z. (2017). Correlation between Network KPIs and User Experience of GSM Networks in Pakistan. Innovative Systems Design and Engineering, Vol. 8, No. 3, 1-10.
Agbolade, O.A. and Oyetunji, S.A. (2016). Voice Conversion Using Coefficient Mapping and Neural Network, in Proceedings of International Conference for Students on Applied Engineering, 20-21 October, Newcastle Upon Tyne, UK, 479-483.
Shafi, I., Ahmed, J., Jamil, A., Shah, S.I. and Kashif, F.M. (2006). Impact of Varying Neurons and Hidden Layers in Neural Network Architecture for a Time Frequency Application, in Proceedings of IEEE International Multitopic Conference, Islamabad, Pakistan, 188-193.
Sheela, K.G. and Deepa, S.N. (2013). Review on Method to Fix Number of Hidden Neurons in Neural Networks, Mathematical Problems in Engineering, Vol. 2013, 1-11.
Jinchuan, K. and Xinzhe, L. (2008). Empirical Analysis of Optimal Hidden Neurons in Neural Network Modeling for Stock Prediction, in Proceedings of the Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2, 828-832.
Popoola, J.J. and Van Olst, R. (2013). Effect of Training Algorithms on Performance of a Developed Automatic Modulation Classification Using Artificial Neural Network, in Proceedings of IEEE AFRICON, Paradise Island, Mauritius, 9-12 September, doi:10.1109/AFRICON.2013.6757676.
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