A Predictive Optimization Model for Path Loss Minimization for GSM Based Network Using Neuro-Swarm Intelligence

International Journal of Computer Science and Engineering

© 2020 by SSRG - IJCSE Journal

Volume 7 Issue 2

Year of Publication : 2020

Authors : Igwe A. R, Anireh V.I.E, Nwaibu N.D

10.14445/23488387/IJCSE-V7I2P103

How to Cite?

Igwe A. R, Anireh V.I.E, Nwaibu N.D, "A Predictive Optimization Model for Path Loss Minimization for GSM Based Network Using Neuro-Swarm Intelligence," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 2, pp. 22-27, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I2P103

Abstract:

Path Loss is the reduction in power density of an electromagnetic wave as it travel, through space. It is a major component in the analysis and design of the telecommunication system. This study presents a predictive optimization model for minimizing path loss in GSM network using Neuro-Swarm Intelligence. Experiment performed include training data collected from GSM network provider using a feed forward propagation algorithm in a system with IPv4 network configuration. Simulation and determination of path loss (signal strength) based on distance, frequency and propagation speed of the data parameters whose termination criteria met the Mean Square Error (MSE). The program was coded in MATLAB. The result obtained was compared favourably with the best two (free space path loss and log normal shadowing model)

Keywords:

Path Loss, Minimization, GSM Network, Optimization, Prediction, Neuro-Swarm Intelligence

References:

[1] Al Salameh M.S.H. and Al Zu'bi M.M. (2015) 'Swarm Intelligence Optimization of Lee Radio-wave Propagation Model for GSM Networks in Irbid
[2] Akande, A. O., Nosiri, C. O., Agubor, K. C. and Okpara, C. R. (2017).Implementation of Particle Swarm Optimization Technique for Enhanced Outdoor Network Coverage in Long Term Evolution Network in Port Harcourt, Nigeria.European Journal of Engineering Research and Science (EJERS), 2(5).
[3] Akpaida, V.O.A., Anyasi F.I., Uzairue S.I., and Idim A.I. (2018) 'Determination of an outdoor Path Loss Model and Signal Penetration Level in some selected Modern Residential and Office Apartment in Ogbomosho, Oyo State, Nigeria.
[4] Anireh, V.I.E and Osegi, E.N.(2019) 'ABC-PLOSS: a software tool for path-loss minimization in GSM telecom networks using artificial bee colony algorithm; Int. J. Swarm Intelligence, 4(1) 20-37.
[5] Arif Sari and Ahmed Alzubi (2018) 'Security and Resilience in Intelligent Data-Centric Systems and Communication Networks'.
[6] Ayeni, A.A., Faruk N., Olawoyin L., Muhammad M.Y. and Gumel M.I (2012) 'Coparative assessments of some selected existing radio propagation models: a study of Kano City, Nigeria; European Journal of Scientific Research', 70(1) 120-127
[7] Carreno E., Romero R. and Padilha Feltrine A. (2008) 'An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem', IEEE Trans Power System, (3) 42-51.
[8] Christopher O. Ahiakwo, Sunny Orike and Otonye E. Ojuka (2018) 'Application of Neuro-Swarm Intelligence Technique ToLoad Flow Analysis'.American Journal of Engineering Research (AJER). 7(8) 94-103.
[9] Faruk N., Ayeni A.A. and Adediran Y.A. (2013) 'Characterization of Propagation Path Loss at VHF/UHF Bands for Ilorin City, Nigeria. Nigeria Journal of Technology. 32(2) 253-265
[10] Figueiras Joao and Frattasi Simon (2010) 'Mobile Positioning and Tracking:FromConversional to Cooperative Techniques' ISBN 1119957567
[11] Messoaud Garah, Houcine Oudira, Lotfi Djouane and Nazih Hamdiken(2017)'Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural Area. International Journal of Electrical and Computer Engineering (IJECE). 7(4) 2125-2131.
[12] Michael U. Onuu and Emem M. Usanga (2017) 'Path Loss Prediction for some GSM Network for AkwaIbom State, Nigeria.
[13] Nwalozie Gerald C., Ufoaroh S.U, Ezeagwu C.O, Ejiofor A.C (2014) 'Path Loss Prediction for GSM Mobile Networks for Urban Region of Aba, South-East Nigeria' International Journal of Computer Science and Mobile Computing (IJCSMC) 3(2) 267-281