Open Access Open Access  Restricted Access Subscription or Fee Access

Effect of Learning Rate on GRNN and MLP for the Prediction of Signal Power Loss in Microcell Sub-Urban Environment

Virginia C. Ebhota(1*), Joseph Isabona(2), Viranjay M. Srivastava(3)

(1) Department of Electronic Engineering, Howard College, University of KwaZulu-Natal, South Africa
(2) Department of Electronic Engineering, Howard College, University of KwaZulu-Natal, South Africa
(3) Department of Electronic Engineering, Howard College, University of KwaZulu-Natal, South Africa
(*) Corresponding author


DOI: https://doi.org/10.15866/irecap.v9i1.15329

Abstract


This research work analyses and presents the effect of learning rate in two Artificial Neural Network (ANN) models: (i) Generalized Regression Neural Network (GRNN), and (ii) Multi-Layer Perceptron (MLP) neural network, used for the prediction of signal power loss in a micro-cell environment. Data has been collected from two separate base stations with Long-Term Evolution (LTE) network are used as inputs for training the models. The GRNN and the MLP neural network are trained using Bayesian Regularization training function and 40 neurons in the hidden layer. The data from the first Base Station (BS1) has been used to investigate the effect of 0.5 to 14 spread factors in GRNN and the effect of utilizing different combination of transfer functions in the hidden and output layers of the MLP model during network training. The data from the second Base Station (BS2) has been used in evaluating the performance of the two models considering their prediction accuracy with different variation of learning rate from 0.2% to 1.4%. Performance metrics such as the Root Mean Square Error, the Mean Absolute Error, the Correlation Coefficient and the Standard Deviation are used for analyzing the results. The results using data from BS1 shows a spread factor of 0.5 with best prediction ability and the least error in GRNN. Small spread factor generalizes a GRNN model excellently even with a high learning rate. Also, hyperbolic tangent and logistic transfer functions in the hidden and output layer of MLP network give the best prediction accuracy for the problem under consideration. The results with data from BS2 show GRNN with the least prediction error at a learning rate of 0.2% with 85% of trained and tested data in the error histogram with 20 bins. The MLP network predicted accurately at a learning rate of 1.0 % with 70 % of the measured data trained and tested in the error histogram with 20 bins.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


Propagation; Artificial Neural Networks; Learning rate; Electromagnetic Waves; Computational Methods of Predicting Propagation; Wireless Communication Networks

Full Text:

PDF


References


F. Nasir, A. A. Adeseko, and A. A. Yunusa, On the study of empirical path loss models for accurate prediction of TV signal for secondary users, Progress in Electromagnetics Research, vol. 49, pp. 155-176, 2013.
https://doi.org/10.2528/pierb13011306

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, vol. 61, pp. 85-117, 2015.
https://doi.org/10.1016/j.neunet.2014.09.003

Noman Shabbir, Muhammad T. Sadiq, Hasnain Kashif, and Rizwan Ullah, Comparison of radio propagation models for long term evolution (LTE) network, Int. J. of Next-Generation Networks, vol. 3, no. 3, pp. 27-41, Sept. 2011.
https://doi.org/10.5121/ijngn.2011.3303

N. S. Nkordeh, A. A. A. Atayero, F. E. Idachaba, and O. O. Oni, LTE network planning using the Hata-Okumura and the COST-231 Hata pathloss models, The World Congress on Engineering (WCE), London, U. K., 2–4 July 2014, pp. 1–5.

N. N. Neskovic, Microcell electric field strength prediction model based upon artificial neural networks, Int. J. Electronics and Communication, vol. 64, pp. 733-738, 2010.
https://doi.org/10.1016/j.aeue.2009.05.005

N. Aleksandar and N. Natasa, Micro cell electric field strength prediction model based upon artificial neural networks, Int. J. of Electronics and Communications, vol. 64, pp. 733–738, 2010.
https://doi.org/10.1016/j.aeue.2009.05.005

E. Amaldi, A. Capone, and F. Malucelli, Radio planning and coverage optimization of 3G cellular networks, Wireless Networks, vol. 14, pp. 435-447, 2008.
https://doi.org/10.1007/s11276-006-0729-3

C. A. Deme, A generalized regression neural network model for path loss prediction at 900 MHz for Jos City, Nigeria, American J. of Engineering Research, vol. 5, pp. 1-7, 2016.

E. Ostlin, H. J. Zepernick, and H. Suzuki, Macrocell path loss prediction using artificial neural networks, IEEE Trans. on Vehicular Technology, vol. 59, pp. 2735-2747, 2010.
https://doi.org/10.1109/tvt.2010.2050502

J. Isabona and Viranjay M. Srivastava, A neural network based model for signal coverage propagation loss prediction in urban radio communication environment, Int. J. of Applied Engineering Research, vol. 11, pp. 11002-11008, Dec. 2016.

Joshua Kahn, Neural network prediction of NFL football games, Lecture Notes, ECE539, Fall 2003.

O. U. Anyama and C. P. Igiri, An application of linear regression and artificial neural network model in the NFL result prediction, Int. J. of Engineering Research & Technology, vol. 4, pp. 457-461, 2015.

C. P. Igiri, O. U. Anyama, and A. I. Silas, Effect of learning rate on artificial neural network in machine learning, Int. J. of Engineering Research & Technology, vol. 4, pp. 395-363, 2015.

Ignacio Rojas, Gonzalo Joya, and Andreu Catala, Advances in Computational Intelligence, Springer International Publishing, Switzerland, 2017.

B. Sharma and K. Venugopalan, Comparison of neural network training functions for Hematoma classification in brain CT images, IOSR J. of Computer Engineering, vol. 16, pp. 31-35, 2014.
https://doi.org/10.9790/0661-16123135

H. B. Celikoglu and H. K. Cigizoglu, Public transportation trip flow modeling with generalized regression neural networks, Advances in Engineering Software, vol. 38, no. 2, pp. 71-79, Feb. 2007.
https://doi.org/10.1016/j.advengsoft.2006.08.003

S. Abdul Hannan, R. R. Manza, and R. J. Ramteke, Generalized regression neural network and radial basis function for heart disease diagnosis, Int. J. of Computer Applications, vol. 7, no. 13, pp. 7–13, Oct. 2010.

Jyh S. R. Jang, Chuen T. Sun, Eiji Mizutani, Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence, 1st Ed., Prentice Hall, USA, 1997.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, vol. 2, no. 5, pp. 359-366, 1989.
https://doi.org/10.1016/0893-6080(89)90020-8

Lawrence J. Landau, Concepts for neural networks - A survey, Springer-Verlag, London, UK, 1998.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, USA, pp. 318-362, 1986.

P. McCullagh and J. A. Nelder, Generalized linear models, 2nd Ed., Chapman & Hall, London, UK, 1989.

W. Duch and N. Jankowski, Survey of neural transfer functions, Neural Computing Surveys, pp. 163-212, 1999.

A. Orriols Puig, J. Castillas, and E. Bernado Mansilla, A comparative study of several genetic-based supervised learning systems, Computational Intelligence, vol. 125, pp. 205-230, 2008.
https://doi.org/10.1007/978-3-540-78979-6_10

Y. Li, Y. Fu, H. Li, and S. W. Zhang, The improved training algorithm of back propagation neural network with self-adaptive learning rate, Int. Conf. on Computational Intelligence and Natural Computing, Wuhan, China, 6–7 June 2009, pp. 73-76.
https://doi.org/10.1109/cinc.2009.111

S. O. Nkakini, M. J. Ayotamuno, S. O. T. Ogaji, and S. D. Probert, Farm mechanization leading to more effective energy-utilizations for Cassava and Yam Cultivations in Rivers State, Nigeria, Applied Energy, vol. 83, no. 12, pp. 1317-1325, Dec. 2006.
https://doi.org/10.1016/j.apenergy.2006.03.001

Rob J. Hyndman and Anne B. Koehler, Another look at measures of forecast accuracy, Int. J. of Forecasting, vol. 22, no. 4, pp. 679-688, Oct.–Dec. 2006.
https://doi.org/10.1016/j.ijforecast.2006.03.001

C. J. Willmott and K. Matsuura, Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Climate Research, vol. 30, no. 1, pp. 79-82, Dec. 2005.
https://doi.org/10.3354/cr030079

S. Ghahramani, Fundamentals of Probability, 2nd Ed., Prentice Hall, New York, USA, 2000.

A. Buda and A. Jarynowski, Life time of correlations and its applications, Wydawnictwo Niezalezne, Poland, pp. 5-21, 2010.

Virginia C. Ebhota, Joseph Isabona and Viranjay M. Srivastava, Base line knowledge on propagation modelling and prediction techniques in wireless communication networks, J. of Engineering and Applied Sciences, vol. 13, no. 7, pp. 1919-1934, 2018.

Virginia C. Ebhota, Joseph Isabona and Viranjay M. Srivastava, Signal power loss prediction based on artificial neural networks in microcell environment, 3rd IEEE Int. Conf. on Electro-Technology for National Development (IEEE NIGERCON), Owerri, Nigeria, 7-10 Nov. 2017, pp. 250-257.
https://doi.org/10.1109/nigercon.2017.8281897

Virginia C. Ebhota, Joseph Isabona, and Viranjay M. Srivastava, Improved adaptive signal power loss prediction using combined vector statistics based smoothing and neural network approach, Int. J. on Progress in Electromagnetics Research C, vol. 82, pp. 155-169, March 2018.
https://doi.org/10.2528/pierc18011203

Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.
https://doi.org/10.15866/irease.v9i4.10220

Aberkane, M., Benouzza, N., Bendiabdellah, A., Boudinar, A., Discrimination between Supply Unbalance and Stator Short-Circuit of an Induction Motor Using Neural Network, (2017) International Review of Automatic Control (IREACO), 10 (5), pp. 451-460.
https://doi.org/10.15866/ireaco.v10i5.11912

Si Tayeb, M., Fizazi, H., A Dual-Level Hybrid Approach for Classification of Satellite Images, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 42-49.
https://doi.org/10.15866/irease.v10i1.11191


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2019 Praise Worthy Prize