International Journal of Computer Network and Information Security(IJCNIS)

ISSN: 2074-9090 (Print), ISSN: 2074-9104 (Online)

Published By: MECS Press

IJCNIS Vol.14, No.3, Jun. 2022

Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

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Odesanya Ituabhor, Joseph Isabona, Jangfa T. zhimwang, Ikechi Risi

Index Terms

Stochastic phenomenon;Neural networks;Adaptive modelling;Adaptive learning;Practical SINR


In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded  forward neural network  model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.

Cite This Paper

Odesanya  Ituabhor, Joseph Isabona, Jangfa T. zhimwang, Ikechi Risi, "Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.3, pp.63-74, 2022. DOI: 10.5815/ijcnis.2022.03.05


[1]J. Isabona, and C.C. Konyeha., "Urban Area Path loss Propagation Prediction and Optimisation Using Hata Model at 800MHz", IOSR Journal of Applied Physics (IOSR-JAP), vol.3, (4), pp.8-18, 2013.

[2]J. Isabona, and D.O Ojuh, Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm I.J. Wireless and Microwave Technologies, 2020, 5, pp.13-24

[3]J. Isabona, C.C. Konyeha, C.B.Chinule, and P.G. Isaiah, "Radio Field Strength Propagation Data and Pathloss calculation Methods in UMTS Network", Advances in Physics Theories and Applications, vol.21,pp. 54-68, 2013.

[4]S.R. Devi, P. Arulmozhivarman, C. Venkatesh, and P. Agarwal, "Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction", International Journal of Automation and Computing vol.13 (5), pp.417-427, October 2016.

[5]W. Budi, S. Rukun S, Suparti, and Y. Hasbi, Cascade Forward Neural Network for Time Series Prediction, Journal of Physics: Conf. Series vol.1025 (2018). doi :10.1088/1742-6596/1025/1/012097

[6]T. Bolanca, S.C. Stefanovic, S. Uki, and M. Rogosic,  Development of Temperature Dependent Retention Models in Ion Chromatography by the Cascade Forward and Back Propagation Artificial Neural Networks, Journal of Chromatography and Related Technologies, vol.32, pp.2765–2778, 2009.

[7]A. Hedayat, H. Davilu, A.A. Barfrosh, and S. Sepanloo, Estimation of research reactor core parameters using cascade feed forward artificial neural networks, Progress in Nuclear Energy, vol.51, 709–718, 2009.

[8]Y. Karaca, Case Study on Artificial Neural Networks and Applications, Applied Mathematical Sciences, vol.10, 45, pp. 2225 – 2237, 2016.

[9]N. Kumar, A. Middey and P.S. Rao, Prediction and examination of seasonal variation of ozone with meteorological parameter through artificial neural network at NEERI, Nagpur, India, Urban Climate, vol.20, pp. 148-167, 2017.

[10]D.C.Montgomery, C.L. Jennings and M. Kulahci, Introduction to Time Series Analysis and Forecasting, John Wiley & Sons, Inc., 2015.

[11]S. Narad, and P. Chavan, "Cascade Forward Back-propagation Neural Network Based Group Authentication Using (n, n) Secret Sharing Scheme", Procedia Computer Science, vol.78, pp.185 – 191, 2016.

[12]A. Pwasong and S. Sathasivam, A new hybrid quadratic regression and cascade forward backpropagation neural network, Neurocomputing vol. 182, pp.197-209, 2016.

[13]S. Tengeleng and N. Armand. "Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution", Atmosphere vol.5, pp.454-472, 2014.

[14]J.P. Zhang and M. Qi, "Neural network forecasting for seasonal and trend time series", European Journal of Operational Research, 160, 501–514, 2005.

[15]S. Ayub and J.P. Saini , ECG classification and abnormality detection using cascade forward neural network, International Journal of Engineering, Science and Technology, vol. 3, No. 3, pp. 41-46, 2011

[16]V. Schetinin, "A Learning Algorithm for Evolving Cascade Neural Networks", Neural Processing Letter vol.17,pp.21-31, 2003. Kluwer

[17]M. L. Iuzzolino, M.C. Mozer, and S. Bengio, Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss, Advances in Neural Information Processing Systems. 2021 Dec 6;34.

[18]Qifei Zhang, Lingjian Fu, Linyue Gu, "A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG", Computational and Mathematical Methods in Medicine, vol. 2019, 2019.

[19]L.B. Nigrini LB, "Developing a Neural Network Model to Predict the Electrical Load Demand in the Mangaung Municipal Area, thesis in the School of Electrical and Computer Systems Engineering, Faculty of Engineering and Information Technology, Central University of Technology, Free State.

[20]Divine O. Ojuh, Joseph Isabona," Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.7, No.3, pp. 12-23, 2021. DOI: 10.5815/ijmsc.2021.03.02

[21]Joseph Isabona, Divine O. Ojuh, "Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.3, pp.35-42, 2021. DOI: 10.5815/ijisa.2021.03.04

[22]3GPP TS 36.214 version 11.1.0 Release 11 (2013), “LTE; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer; Measurements”. ETSI TS 136 214 V11.1.0 (2013-02), pp.1-15.

[23]J. Isabona, and D.O Ojuh, "Modelling based Quantitative Assessment of Operational LTE Mobile Broadband Networks Reliability: a Case Study of University Campus Environ", IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), vol. 15(1).pp. 22-31,2020

[24]Marquardt, D., “An algorithm for least-squares estimation of nonlinear parameters,” SIAM Journal on Applied Mathematics, Vol. 11, No. 2, 431–441, DIO:10.1137/0111030, 1963.

[25]V,C. Ebhota, J. Isabona and V.M. Srivastava, V.M. "Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss, Prediction in Cluttered and Open Urban Microcells", Wireless Personal Communications, Vol. 104 (3), pp. 935–948

[26]J. Isabona Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction. Wireless Pers Commun, vol 114, pp. 3635–3653 (2020).