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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.13, No.4, Aug. 2021

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

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Author(s)

Divine O. Ojuh, Joseph Isabona

Index Terms

Electric field strength;outliers;estimation accuracy;highly stochastic;heteroskedastic;Weighted;least square regression

Abstract

Propagated electromagnetic signal over the cellular radio communication channels and interfaces are usually highly stochastic and complex with unequal noise variation pattern. This is due to multipath nature of the propagation channels and diverse radio propagation mechanisms that impact the signal strength at the receiver en-route the transmitter, and verse versa. This also makes measurement, predictive modeling and estimation based analysis of such signal very challenging and complex as well. One important and popular parametric modelling and estimation technique in mathematics and engineering science, especially for signal processing applications is the least square regression (LSR). The dominance use and popularity of the LSR approach may be attributed to its simplified supporting theory, relatively fast application procedure and ubiquitous application packages. However, LSR is known to be very sensitive to outliers and unusual stochastic signal data. In this work, we propose the application of weighted least square regression method for enhanced propagation practical field strength estimation modelling over cellular radio communication networks interface. The signal data was collected from a commercial LTE networks service provider. Also, we provide statistical computational analyses to compare the resultant estimation outcome of the weighted least square and the standard least approach. From the result, it is found that the WLSR approach is reliably better the most popular standard least square method. The significance and academic of value of this paper is that our proposed and implemented WLSR method can used as replacement of the standard LSR approach for robust mobile signal processing of future communication system networks.

Cite This Paper

Divine O. Ojuh, Joseph Isabona, "Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.4, pp.14-23, 2021. DOI: 10.5815/ijisa.2021.04.02

Reference

[1]Isabona, J, and Srivastava, V.M. (2017), Radio Channel Propagation Characterization and Link Reliability Estimation in Shadowed Suburban Macrocells, International Journal on Communications Antenna and Propagation (IRECAP), Vol. 7 (1), pp. 57-63.

[2]Isabona, J, and Ojuh, D. O. (2017) Wavelet Selection Based on Wavelet Transform for optimum Noisy Signal Processing, International Journal of Basic and Applied Sciences, Vol. 3, Issue 1, pp. 57-65 ©Faculty of Basic and Applied Sciences, Benson Idahosa University, Benin City, Nigeria.

[3]C. K. Mai, I. V. M. Krishna, and A. V. Reddy, “Polyanalyst application for forest data mining,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’05), vol. 2, pp. 756–759, July 2005.

[4]Y. Zhou, S. C. Huang, and M. Bergsneider, “Linear ridge regression with spatial constraint for generation of parametric images in dynamic positron emission tomography studies,” IEEE Transactions on Nuclear Science, vol. 48, no. 1 I, pp. 125–130, 2001.

[5]Y. Zhou, S. C. Huang, and Y. I. Hser, “Generalized ridge regression versus simple ridge regression for generation of kinetic parametric images in PET,” in Proceedings of IEEE Nuclear Science Symposium, vol. 3, pp. 1551–1555, 1999.

[6]C. K. Mai, I. V. M. Krishna, and A. V. Reddy, “Polyanalyst application for forest data mining,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’05), vol. 2, pp. 756–759, July 2005.

[7]G. Sebastiani, F. Godtliebsen, R. A. Jones, O. Haraldseth, T. B. M¨uller, and P. A. Rinck, “Analysis of dynamic magnetic resonance images,” IEEE Transactions on Medical Imaging, vol. 15, no. 3, pp. 268–277, 1996.

[8]K. Diawuo,K., Dotche, K.A and T Cumberbatch, ''Data Fitting to Propagation Model Using Least Square Algorithm: A Case Study in Ghana'', International Journal of Engineering Sciences, 2(6), 226-230, 2013.

[9]J. Isabona, and S.O. Azi. S.O ''Optimised Walficsh-Bertoni Model for Pathloss Prediction in Urban Propagation Environment'', International Journal of Engineering and Innovative Technology (IJEIT) Vol. 2, Issue 5, pp 14-20, 2012.

[10]J.Isabona and C.C. Konyeha. ''Experimental Study of UMTS Radio Signal Propagation Characteristics by Field Measurement'', American Journal of Engineering Research, vol. 2, (7), pp 99-106.

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

[12]J. Isabona and G.P. Isaiah. ''CDMA2000 Radio Measurements at 1.9GHz and Comparison of Propagation Models in Three Built-Up Cities of South-South, Nigeria'', American Journal of Engineering Research (AJER), Vol. 2, Issue-05, pp-96-106, 2013.

[13]J. Isabona, and S.O. Azi. S.O ''Enhanced Radio Signal Loss Prediction with Correction Factors for Urban Streets in the IMT-2000 Band'', Elixir Space Science, vol. pp.15958-15962, 2013.

[14]J. Isabona, and M. Babalola, ''Statistical Tuning of Walfisch-Bertoni Pathloss Model based on Building and Street Geometry Parameters in Built-up Terrains''. American Journal of Physics and Applications, vol. 1, pp. 10-16, 2013. 

[15]J. Isabona, C.C. Konyeha, C.B. Chinule and G.P. 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.

[16]J. Isabona,.G. P. Isaiah ‘‘Computation and Verification of Propagation Loss Models based on Electric Field Data in Mobile Cellular Networks, Australian Journal of Basic and Applied Sciences, Vol. 9 (29), pp 280-285, 2015.

[17]Isabona Joseph, Divine O. Ojuh, " Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.10, No.5, pp. 13-24, 2020.DOI: 10.5815/ijwmt.2020.05.02

[18]M.K Yeliz, ''Generalized Least Square and Weighted Least Squares Estimation Methods for Distributional Parameters''. REVSTAT – Statistical Journal, Vol. 13 (3), pp.263–282, November 2015. 

[19]F. H. Thanoon. ''Robust Regression by Least Absolute Deviations Method'' International Journal of Statistics and Applications, 5(3): 109-112, 2015.  DOI: 10.5923/j.statistics.20150503.02 

[20]Y.M Kantar, Generalized Least Squares and Weighted Least Squares Estimation Methods for Distributional Parameters, REVSTAT – Statistical Journal Vol. 13 (3), 263–282, 2014.

[21]J. Isabona, ''Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction'', Wireless Personal Communication (Springer) https://doi.org/10.1007/s11277-020-07550-5, 2020.

[22]I.B. Oluwafemi, and O. J. Femi-Jemilohun.''Propagation Profile and Strength Variation of VHF Signal in Ekiti State Nigeria'', .J. Wireless and Microwave Technologies, Vol 7, No.3, pp. 9-24, 2017. 

[23]Akinsanmi Akinbolati, Olufemi J. Agunbiade, " Assessment of Error Bounds for Path Loss Prediction Models for TV White Space Usage in Ekiti State, Nigeria", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.3, pp. 28-39, 2020. DOI: 10.5815/ijieeb.2020.03.04