EMPIRICAL MOBILE NETWORK TRAFFIC PREDICTION: STATISTICAL COMPARATIVE PERFORMANCE ANALYSIS USING MIMO RBFN NETWORK MODEL
Keywords:4G, 3G, radial basis function neural network, multiple-input and multiple-output, forecasting
The exponential demand of telecommunication traffic require the development of different forecasting models in order to help industry players to plan for the future. The available forecasting traffic models are mostly developed for single-input single-output traffic data. The study applies multiple-input multiple-output (MIMO) radial basis neural network (RBFNN) model to instantaneously forecast five different time spans of telecommunication network traffic obtained from 4G and 3G networks operators. The data was taken from 3G uplink hourly, 3G daily voice, 4G weekly, 3G downlink monthly and 3G downlink quarterly from 2015 to 2017. The results prove that MIMO RBFNN (5-10-5) gives higher prediction accuracy than the other three MIMO models when subjected to different statistical tests.
Al-Mayyahi, A., Wang, W., Birch, P. (2015): Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles. – International Journal of Mechatronics and Automation 5(2-3): 140-153.
Anifowose, F.A. (2010): A Comparative Study of Gaussian Mixture Model and Radial Basis Function for Voice Recognition. – International Journal of Advanced Computer Science and Applications (IJACSA) 1(3): 9p.
Biernacki, A. (2017): Analysis and modelling of traffic produced by adaptive HTTP-based video. – Multimedia Tools and Applications 76(10): 12347-12368.
CISCO Official Portal (2020): Cisco Annual Internet Report (2018-2021). – CISCO Official Portal. Available on:
Dash, C.S.K., Behera, A.K., Dehuri, S., Cho, S.B. (2016): Radial basis function neural networks: a topical state-of-the-art survey. – Open Computer Science 6(1): 33-63.
Ebrahimzadeh, A., Khazaee, A. (2010): Detection of premature ventricular contractions using MLP neural networks: A comparative study. – Measurement 43(1): 103-112.
Franco, D.B., Steiner, M.T.A. (2017): New strategies for initialization and training of radial basis function neural networks. – IEEE Latin America Transactions 15(6): 1182-1188.
Giveki, D., Rastegar, H. (2019): Designing a new radial basis function neural network by harmony search for diabetes diagnosis. – Optical Memory and Neural Networks 28(4): 321-331.
Huang, C.H. (2014): Modified neural network for dynamic control and operation of a hybrid generation systems. – Journal of Applied Research and Technology 12(6): 1154-1164.
Iqbal, M.F., Zahid, M., Habib, D., John, L.K. (2019): Efficient prediction of network traffic for real-time applications. – Journal of Computer Networks and Communications 11p.
Li, J., Jia, Z., Qin, X., Sheng, L., Chen, L. (2013): Telephone traffic prediction based on modified forecasting model. – Research Journal of Applied Sciences, Engineering and Technology 6(17): 3156-3160.
Marček, D., Square, B. (2015): Hybrid arima/rbf framework for prediction bux index. – Journal of Computer and Communications 3(05): 9p.
Mohanty, C.S., Khuntia, P.S., Mitra, D. (2014): Momentum Based radial basic function neural controller for Pitch control of an Aircraft. – Computer Sciences and Telecommunications 47(1): 30-37.
Ozovehe, A., Okereke, O.U., Chibuzo, A.E., Usman, A.U. (2018): Comparative analysis of traffic congestion prediction models for cellular mobile macrocells. – European Journal of Engineering and Technology Research 3(6): 32-38.
Ramesh, J., Vanathi, P.T., Gunavathi, K. (2008): Fault Classification in Phase‐Locked Loops Using Back Propagation Neural Networks. – ETRI journal 30(4): 546-554.
Santhanam, T., Subhajini, A.C. (2011): An efficient weather forecasting system using radial basis function neural network. – Journal of Computer Science 7(7): 962-966.
Soares Mayer, K., Aguiar Soares, J., Soares Arantes, D. (2020): Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels. – Sensors 20(2): 378p.
Staiano, A., Inneguale, F. (2017): An RBF neural network-based system for home smart metering. – In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 6p.
Suzuki, K. (2011): Artificial Neural Network: Methodological Advances and Biomedical Applications. – IntechOpen 374p.
Szmit, M., Szmit, A., Kuzia, M. (2013): Usage of RBF Networks in prediction of network traffic. – In FedCSIS (Position Papers) 3p.
Tripura, J., Roy, P., Barbhuiya, A.K. (2018): Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting. – Journal of Engineering & Technological Sciences 50(3): 434-449.
Wang, J., Wang, J., Fang, W., Niu, H. (2016): Financial time series prediction using elman recurrent random neural networks. – Computational Intelligence and Neuroscience 14p.
Wysocki, A., Ławryńczuk, M. (2016): Elman neural network for modeling and predictive control of delayed dynamic systems. – Archives of Control Sciences 26(1): 117-142.
Xing, S., Lou, Y. (2019): Hydrological time series forecast by ARIMA+ PSO-RBF combined model based on wavelet transform. – In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 5p.
Xu, X.Z., Ding, S.F., Shi, Z.Z., Zhu, H. (2012): Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm. – Journal of Zhejiang University SCIENCE C 13(2): 131-138.
Zaleski, A., Kacprzak, T. (2010): Packet switching networks traffic prediction based on radial basis function neural network. – Journal of Applied Computer Science 18(2): 91-101.
Zhang, X., You, J. (2020): A gated dilated causal convolution based encoder-decoder for network traffic forecasting. – IEEE Access 8: 6087-6097.
Zhang, C., Patras, P., Haddadi, H. (2019): Deep learning in mobile and wireless networking: A survey. – IEEE Communications Surveys & Tutorials 21(3): 2224-2287.
Zhang, P., Wang, L., Li, W., Leung, H., Song, W. (2017): A web service qos forecasting approach based on multivariate time series. – In 2017 IEEE International Conference on Web Services (ICWS) 8p.