Open Access Open Access  Restricted Access Subscription or Fee Access

Prediction of Signal Power Loss (at Micro-Cell Environments) Using Generalized Adaptive Regression and Radial Basis Function ANN Models Built on Filter for Error Correction


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecap.v11i2.19964

Abstract


The Prediction of an accurate field signal power is of utmost importance in the design and placement of base station transmitters. This research work has designed and utilized two adaptive neural network models named Generalized Adaptive Regression (GR)-ANN and Radial Basis Function (RBF)-ANN made on vector Non-Linear Median Filter (NLMF) and it has compared their prediction performances with conventional GR-ANN and RBF-ANN. The prediction accuracy of the neural network models has been tested and evaluated using measurement experimental field strength data acquired from the LTE radio network from the line-of-sight (LOS) urban environment named location-1 Non-Line-of-Sight (NLOS) rural environment named location-2. Prediction error, average spread prediction error, and mean squared error have been used for analyzing the performance abilities of the models in the prediction of the measurement data during neural network training. Their performance accuracies have been compared. The GR-ANN and RBF-ANN's superior performances built on vector NLMF over the conventional GR-ANN, and RBF-ANN can be attributed to the high-quality datasets generated through filtering using NLMF. This improves the adaptive models' capability to learn, adaptively respond, and predict the reference propagation loss data's fluctuating pattern during neural network training. A further comparison shows GR-ANN's superiority in performance accuracy built on vector NLMF over RBF-ANN built on vector NLMF. This shows that GR-ANN's capability of solving any function approximation problem is better than RBF-ANN. However, RBF-ANN requires less training time.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Base Station; Signal Power; Signal Prediction; Generalized Adaptive Regression; Radial Basis Function; Artificial Neural Network; Filters

Full Text:

PDF


References


J. Isabona and C. C. Konyeha, Urban area path loss propagation prediction and optimisation using Hata model at 800MHz, Journal of Applied Physics vol. 3, pp. 08-18, 2013.
https://doi.org/10.9790/4861-0340818

T. S. Rappaport, Wireless communications: Principles & practice, 2nd Ed. Upper Saddle River, New Jersey: Prentice Hall 1996.

V. C. Ebhota, J. Isabona, and V. M. Srivastava, Signal power loss prediction based on artificial neural networks in microcell environment, 3rd IEEE International Conference on Electro-Technology for National Development, Owerri, Nigeria, 2017.
https://doi.org/10.1109/nigercon.2017.8281897

H. Andrej, K. Gorazd, and J. Tomaz, A Survey of radio propagation modeling for tunnels, IEEE Communications, Surveys & Tutorials vol. 16, pp. 658-669, 2014.
https://doi.org/10.1109/surv.2013.091213.00175

J. Sumit, Outdoor propagation models - A literature review, International Journal on Computer Science and Engineering, vol. 4, pp. 281-291, 2012.

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

C. Phillips, D. Sicker, and D. Grunwald, A survey of wireless pathloss prediction and coverage mapping methods, IEEE Commun. Surv. Tutor, vol. 15, pp. 255-270, 2013.
https://doi.org/10.1109/surv.2012.022412.00172

P. Sridhar, Novel artificial neural network path loss propagation models for wireless communications, Advances in Wireless and Mobile Communications, vol. 10, pp. 233-237, 2017.

J. Ramkumar and R. Gunasekaran, A new path loss model for LTE network to address propagation delay, International Journal of Computer and Communication Engineering vol. 2, pp. 413-416, 2013.
https://doi.org/10.7763/ijcce.2013.v2.216

C. Stergiou and D. Siganos, Neural networks, Sunrise Journal, vol. 4, 2015.

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

V. C. Ebhota, J. Isabona, and V. M. Srivastava, Improved adaptive signal power loss prediction using combined vector smoothing and neural network approach, Progress in Electromagnetic Research C, vol. 82, pp. 155-169, 2018.
https://doi.org/10.2528/pierc18011203

C. P. Igiri and E. O. Nwachukwu, An improved prediction system for football a match result, Journal of Engineering, vol. 4, pp. 12-20, 2014.

B. J. Cavalcanti and G. A. Cavalcante, A hybrid path loss prediction model based on artificial neural networks using empirical models for LTE And LTE-A at 800 MHz and 2600 MHz, Journal of Microwaves, Optoelectronics and Electromagnetic Applications, vol. 16, pp. 704-718, 2017.
https://doi.org/10.1590/2179-10742017v16i3925

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

J. Isabona and V. M. Srivastava, Hybrid neural network approach for predicting signal propagation loss in urban microcells networks, International Journal of Applied Engineering Research, vol. 11, pp. 11002-11008, 2016.
https://doi.org/10.1109/r10-htc.2016.7906853

Triqui, B., Benyettou, A., Comparative Study Between Radial Basis Function and Temporal Neuron Network Basic in Cardiac Arrhythmia, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (2), pp. 186-193.
https://doi.org/10.15866/irecap.v8i2.14079

V. C. Ebhota, J. Isabona, and V. M. Srivastava, Base line knowledge on propagation modelling and prediction techniques in wireless communication networks, Journal of Engineering and Applied Sciences, vol. 13, pp. 1919-1934, 2018.

G. Li, H. Alnuweiri, Y. Wu, and H. Li, Acceleration of back propagation through initial weight pre-training with delta rule, in Proc. IEEE Int. Conf. Neural Network World, 1993, pp. 580-585.
https://doi.org/10.1109/icnn.1993.298622

Z. Jianfeng, L. Qianlong, Z. Xingyao, K. Wolfgang, and C. Ji, Prediction of MRI RF exposure for implantable plate devices using artificial neural network, IEEE Transation on Electromagnetic Compatibility, pp. 1-9, 2019.

S. Karsoliya, Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture, Int. J. Eng. Trends Technol, vol. 3, pp. 714-717, 2012.

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural Network World, vol. 2, pp. 359- 366, 1989.
https://doi.org/10.1016/0893-6080(89)90020-8

J. A. Snyman, Practical mathematical optimization: An introduction to basic optimization theory and classical and new gradient-based algorithms, 2nd ed. New York: Springer, 2005.

D. J. C. MacKay, A practical Bayesian framework for back-propagation networks, Neural Computation, vol. 4, pp. 448-472, 1992.

V. C. Ebhota, J. Isabona, and V. M. Srivastava, Investigating Signal Power Loss Prediction in A Metropolitan Island Using ADALINE and Multi-Layer Perceptron Back Propagation Networks, International Journal of Applied Engineering Research, vol. 13, pp. 13409-13420, 2018.

C. S. Kumar Dash, A. Kumar Behera, S. Dehuri, and S. B. Cho, Radial basis function neural networks: A topical state-of-the-art survey, Open Computer Science, vol. 6, pp. 33-63, 2016.
https://doi.org/10.1515/comp-2016-0005

H. S. Abdul, R. R. Manza, and R. J. Ramteke, Generalized regression neural network and radial basis function for heart disease diagnosis, International Journal of Computer Applications vol. 7, 2010.
https://doi.org/10.5120/1325-1799

Ebhota, V., Isabona, J., Srivastava, V., Investigation and Comparison of Generalization Ability of Multi-Layer Perceptron and Radial Basis Function Artificial Neural Networks for Signal Power Loss Prediction, (2019) International Journal on Communications Antenna and Propagation (IRECAP), 9 (1), pp. 46-54.
https://doi.org/10.15866/irecap.v9i1.15330

J. B. Anderson, T. S. Rappaport, and S. Yoshida, Propagation measurements and models for wireless communications channels., IEEECommunMag, vol. 33, pp. 42-9, 1995.

Matteo Maggioni, Vladimir Katkovnik, Karen Egiazarian, and Alessandro Foi, Nonlocal transform-domain filter for volumetric data denoising and reconstruction, IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 119-133, Jan. 2013.
https://doi.org/10.1109/tip.2012.2210725

V. C. Ebhota, J. Isabona, and V. M. Srivastava, Environment-adaptation based hybrid neural network predictor for sgnal propagation loss prediction in cluttered and open urban microcells, Wireless Personal Communications, vol. 104, pp. 935-948, 2019.
https://doi.org/10.1007/s11277-018-6061-2

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

Christopher A. Metzler, Arian Maleki, and Richard G. Baraniuk. From denoising to compressed sensing, IEEE Transactions on Information Theory, vol. 62, no. 9, pp. 5117-5144, Sept. 2016
https://doi.org/10.1109/tit.2016.2556683

W. Chen and K. Chau, Intelligent manipulation and calibration of parameters for hydrological models, Int. Journal on. Environ. Pollut, vol. 28, pp. 432-447, 2006.

N. M. Nawi, W. H. Atomi, and M. Z. Zehman, The Effect of data pre-processing on optimized training of artificial neural Networks, Procedia Technology, vol. 11, pp. 32-39, 2013.
https://doi.org/10.1016/j.protcy.2013.12.159

K. A. Dotche, F. Sekyere, and W. Banuenulmah, LPC for Signal analysis in cellular network coverage, Open access library Journal, vol. 3, pp. 1-10, 2016.
https://doi.org/10.4236/oalib.1102759

A. Ali, R. Ghazali, and M. Mat Deris, The wavelet multilayer perception for the prediction of earthquake time series data, in Proceedings of the 13th International conference on information integration and web-based applications and services, Ho Chi Minh City,Vietnam, 2011, pp. 138-143.
https://doi.org/10.1145/2095536.2095561

X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool, Dynamic filter networks, 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 2016.

C. L. Wu, K. W. Chau, and C. Fan, Prediction of rainfall time series using modular artificial neural networks coupled with data pre-processing techniques, Journal of Hydrology, vol. 389, pp. 146-167, 2010.
https://doi.org/10.1016/j.jhydrol.2010.05.040

V. Jothiprakash and A. S. Kote, Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow, Journal of Hydrol. Sci., vol. 56,, pp. 168-186, 2011.
https://doi.org/10.1080/02626667.2010.546358

R. V. Ramana, B. Krishna, S. R. Kumar, and N. G. Pandey, Monthly rainfall prediction using wavelet neural network analysis, International Journal of Water Resource Management, vol. 27, pp. 3697-3711, 2013.
https://doi.org/10.1007/s11269-013-0374-4

C. C. Chou, A threshold based wavelet denoising method for hydrological data modelling, International Journal of Water Resource Management, vol. 25, pp. 1809-1830, 2011.
https://doi.org/10.1007/s11269-011-9776-3

J. Adamowski and K. Sun, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, Journal of Hydrology, vol. 390, pp. 85-91, 2010.
https://doi.org/10.1016/j.jhydrol.2010.06.033

D. Wu, J. Wang, and Y. Teng, Prediction of under-groundwater levels using wavelet decompositions and transforms, Journal of Hydrology Engineering, vol. 5, pp. 34-39, 2004.

V. Jothiprakash and A. S. Kote, Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow, Journal of Hydrol. Sci., vol. 56, pp. 168-186, 2011.
https://doi.org/10.1080/02626667.2010.546358

N. N. Vijayakumar and B. Plale, Prediction of missing events in sensor data streams using Kalman Filters, in Proceedings of the 1st Int’l workshop on knowledge discovery from sensor data in conjunction with ACM 13th Int’l conference on knowledge discovery and data mining, 2007, pp. 1-9.
https://doi.org/10.1201/9781420082333.ch9

B. D. Brabandere, X. J. Tuytelaars, T., and L. V. Gool, Dynamic filter networks, 30th Conference on Neural Information Processing Systems, 2016.

V. R. Tripathi, Image denoising using non-linear filter, International Journal of Modern Engineering Research, vol. 2, pp. 4543-4546, 2012.

N. R. Kumar and J. U. Kumar, A spatial mean and median filter for noise removal in digital images, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, pp. 246-253, 2015.
https://doi.org/10.15662/ijareeie.2015.0401037

V. C. Ebhota and V. M. Srivastava, Performance analysis of learning rate parameter on prediction of signal power loss for network optimization and better generalization, Wireless Personal Communications, vol. 116, 2021.
https://doi.org/10.1007/s11277-020-08061-z

R. Jain, R. Kasturi, and B. G. Schunck, Image processing fundamentals, McGraw-Hill International Edition, 1995.

G. Bhumika and S. N. Shailendra, Image denoising with linear and non-linear filters: A review, International Journal of Computer Science Issues, vol. 6, pp. 169-154, 2013.

A. K. Jain, Fundamentals of digital image processing. Englewood Cliffs: NJ: Prentice Hall, 1989.

R. Jain, R. Kasturi, and B. G. Schunck, Image processing fundamentals: McGraw-Hill International Edition, 1995.

R. Gonzalez and R. Woods, Digital image processing, Wesley, New York.: Adison 1992.

W. Ye and Z. Liao, Generalized correlativity of median filtering operator on signals, Open Journal of Discrete Mathematics, vol. 2, pp. 83-87, 2015.
https://doi.org/10.4236/ojdm.2012.23015

R. Lukac, K. N. Plataniotis, and B. Smolka, Generalized selection weighted vector filters, EURASIP Journal on Applied Signal Processing, vol. 12, pp. 1870-1885, 2004.
https://doi.org/10.1155/s1110865704312126

D. Marquardt, An algorithm for least-squares estimation of non-linear parameters, SIAM Journal on Applied Mathematics, vol. 11, pp. 431-441, 1963.

Joao F. Ferreira, Jorge Lobo, Pierre Bessiere, Miguel C. Branco, and Jorge Dias, A Bayesian framework for active artificial perception, IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 699-711, April 2013.
https://doi.org/10.1109/tsmcb.2012.2214477

Foresee and Hagan, Gauss-Newton approximation to Bayesian regularization, Proceedings of the International Joint Conference on Neural Networks, 1997.

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

S. Ghahramani, Fundamentals of Probability, 2nd Ed., New Jersey: Prentice Hall, 2000.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize