Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator

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Divine O. Ojuh 1,* Joseph Isabona 2

1. Department of Physical Sciences, Faculty of Sciences, Benson Idahosa University, Benin City, Edo State

2. Department of Physics, Faculty of Science, Federal University Lokoja, PMB. 1154, Lokoja, Kogi State

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2021.03.02

Received: 18 Mar. 2021 / Revised: 15 Apr. 2021 / Accepted: 10 May 2021 / Published: 8 Aug. 2021

Index Terms

Block calls, Drop calls, modelling, optimal probability distribution, goodness-of-fit, log-logistic, prognostic analysis


Mobile phones and handsets enable us to communicate our voice, data and video messages with individuals that are far-off from us. When an active call is initiated by someone using a mobile phone, it is transmitted through a nearby Base Station (BS) transmitter to another BS until the call gets to its intended receiver. Any time a caller initiates and loses a connection to a BS while on conversation, the call is said to be dropped. The initiation and completion of an active call without any form of disconnection or termination is a key service quality parameter in telecommunication system networks. Robust statistical estimation, modelling and characterization of call drop rates is of high importance to the network operators and radio frequency engineers for effective re-planning and performance management process of telecommunication system networks. This work was designed to determine the optimal probability distribution model for drop call rates based on a five week acquired rate of drop calls data sample in the Southern regions of Nigeria.  To accomplish the aim, eight probability distributions namely logistic, log-logistic, normal, log-normal, exponential, Rayleigh, rician and Gumbel max were explored and based on the combined scores of three goodness of fit statistical tests, the log-logistic distribution was found to be the optimal probability distribution for the weekly rate of drop call prognostic analysis. The results could be of immense assistance to radio frequency engineers for optimal statistical modelling and design of cellular systems channels. 

Cite This Paper

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


[1]Soldani, D, Li,Mand Cuny, R. "QoS and QoE Management in UMTS Cellular Systems“, John Wiley & Sons, Chichester, UK, 2006. 

[2]Islam, M.S.  and Niaz, A.Z.M,  ”Analysis of Service Quality and Satisfaction Level of Customers in Backing Sector of Banglandesh“, British Journal of Marketing Studies Vol.2, No.7, pp.14-28, November 2014

[3]Isabona, J and Srivastava, V.M, "User-Centric Methodology for Objective Assessment of Service Quality in Established Wireless Mobile Communication Networks", International Journal on Communications Antenna and Propagation, Vol. 7, (1), pp. 226-30, 2017. DOI: 10.15866/irecap.v7i1.10475 26 

[4]Isabona, J and Ekpenyong, M, ”End-User Satisfaction Assessment Approach for Efficient Network Performance Monitoring in Wireless Communication Networks", African Journal of Computing and ICT, vol. 8. No. 1, pp. 1-16.

[5]Sudhindra, K.R and  Sridhar, V, “Root cause detection of cal drops in live GSM”, IEEE TENCON 2011. 

[6]Fang, Y, "Modeling and Performance Analysis for Wireless Mobile Networks: A New Analytical Approach”, IEEE/ACM Transactions on Networking, vol. 13 (5), pp. 989-1002, 2005.

[7]Praveen Kumar ,Vinay Prakash Sriwastava, Rishi Srivastava “Decreasing Call Blocking and Dropping Rate by Implementing Resource Planning Model Through Auxiliary Station in Search MODE”“Computer Science and Engineering BBD University Lucknow, India ” I JIRSE Journals Vol 2,Issue-5–May 2014 

[8]Allam Moousa, n-Najah National University, Palestine, “Prioritization Schemes in Queuing Handoff and New Calls to Reduce Call Drops in Cellular System” 52 International Journal of Mobile Computing and Multimedia Communications-3(2),52-61,April-june 2011 

[9]Dajab, D.D, Tarka, S.N. and Bajoga, B.G, “Simulation and Analysis of Drop-call Probability Model: A Case Study of MTEL”, Nigerian Journal of Engineering, Vol. 16, No. 1, 2009

[10]Sun, H and Williamson,C, “Simulation Evaluation of Call Dropping Policies for Stochastic Capacity Networks”, Department of Computer Science University of Calgary, Calgary, AB, Canada T2N 1N4, 2005.

[11]Boggia, G, Camarda, P. and D’Alconzo, A,  “Modeling of Call Dropping in Well-Established Cellular Networks”, EURASIP Journal on Wireless Communications and Networking, Vol. 2007, Article ID 17826, October, 2007

[12]Boggia, G., Camarda, P., D’Alconzo, A. De Biasi , A. and Siviero, M,  “Drop Call Probability in Established Cellular Networks: from Data Analysis to Modeling”, DEE – Politecnico di Bari, Via E. Orabona, 4 – 70125 Bari (Italy), Proc. IEEE VTC Spring 2005.,Vol. 5, pp2775-2779, 2005

[13]Ekpenyong M, and Isabona, J, "An Enhance SINR- Based Call admission Control in 3G Networks", International Journal of Wireless and Mobile Networks (IJWMN) Vol. 3, No. 5, pp 49-64, 2011.

[14]Atenaga, M and Isabona, J, “On the Compromise between Network Performance and End User Satisfaction over UMTS Radio Interface: An Empirical Investigation, International Journal of Advanced Research in Physical Science (IJARPS)" Vol. 1, Issue 7, PP 9-18, 2014.

[15]Isabona, J, and and Obahiagbon, K, ”A Practical Optimisation method to Improve QoS and GoS Based Key Performance Indicators in GSM Network Cell Cluster Environment“, International Journal of Wireless and Mobile Networks, (IJWMN) Vol. 6, No. 5, pp 93-107, 2014.

[16]Isabona, J "Maximizing Coverage and Capacity with QoS Guarantee in GSM Network by means of Cell Cluster optimization", International Journal of Advanced Research in Physical Science (IJARPS) Vol. 1, Issue 6, PP 44-55, 2014.

[17]Isabona, J, ”Real time Monitoring of Service Quality of a deployed UMTS Wireless Network in Campus Environment-An Optimization Perspective", International Journal of Information Science and Systems, vol.2, No.3, pp 1-16, 2013.

[18]Isabona, J. “Maximizing Coverage and Capacity with QoS Guarantee in GSM Network by means of Cell Cluster optimization", International Journal of Advanced Research in Physical Science (IJARPS) Vol. 1, Issue 6, PP 44-55,, 2014.

[19]Isabona, J, and Srivastava, V.M, ”Real-Time Signal Coverage and Quality Monitoring Towards Performance Optimisation in Contemporary Mobile Broadband Cellular Networks", Research Journal of Applied Sciences Vol. 13 (4), pp. 235-240, (2018).

[20]Elizabeth N. Onwuka, Michael Okwori, Salihu O. Aliyu, Stephen S. Oyewobi, Caroline O. Alenoghena, Habeeb Bello-Salau, Sani S. Makusidi, and Victor Asuquo," Survey of Cellular Signal Booster", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.6, pp. 21-31, 2018. DOI: 10.5815/ijieeb.2018.06.03

[21]Isabona, J.and and Oghu, E., “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).22-31, 2020. DOI: 10.9790/2834-1501012231

[22]Bamidele Moses Kuboye, Boniface Kayode Alese, Olumide Sunday Adewale, Samuel Oluwole Falaki,"Multi-Level Access Priority Channel Allocation with Time Threshold in Global System for Mobile Communications (GSM) Networks", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.11, pp.17-28, 2015. DOI: 10.5815/ijitcs.2015.11.03

[23]Vincent Omollo Nyangaresi, Silvance Abeka, Anthony Rodrigues, " Multivariate Probabilistic Synthesis of Cellular Networks Teletraffic Blocking with Poissonian Distribution Arrival Rates", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.8, No.4, pp. 14-39, 2018.DOI: 10.5815/ijwmt.2018.04.02

[24]Ekpenyong M, and Isabona, J. ”Performance Modeling of Blocking Probability in Multihop Wireless Networks“, Journal of applied science & engineering technology, vol.2, pp 33-41, 2011.

[25]Ekpenyong, M and Isabona, J, "Modeling the Effect of Dropped Calls Cell Traffic in Established 3G-Based Cellular Networks", African Journal of Computing and ICT, vol. 7, No. 2, pp 157-163, 2014.

[26]Ekpenyong M, and Isabona, J. (2011). ”Quantifying GoS and QoS in CDMA cellular networks“,Elixir Network Engg. (34) 2630-2635. www.elixirjournal.org

[27]Ekpenyong M.E and Isabona, J, "Traffic Delay Estimation for 3G Mobile IP Services”, World Journal of Applied Science and Technology, Vol.4. No 2, pp 158-166, 2012.

[28]Ekpenyong M, Eromosele.G and Isabona, J, "On the performance modeling of outage probability in CDMA wireless networks“, Journal of Engineering Science and Technology Review, vol.4 (1) 74-82, 2011.

[29]Isabona, J, Azi. S.O and Ekpenyong, M., (2011). Enhanced Spectral Utilization of 3G WCDMA-Based FDD Mode in the Uplink Transmission, Modern Applied Science www.ccsenet.org/mas Vol. 5, No. 1; pp 117-132.

[30]Ojuh.O. Divine and Isabona, J, ”Application of Supervised Machine Learning Based on Gaussian Process Regression for Extrapolative Cell Availability Evaluation in Cellular Communication Systems“. Communications in Computer and Information Science, book Series, CCIS vol. 1350, pp. 93-106, 2021. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_11.

[31]Isabona, J, "Parametric Maximum Likelihood Estimator combined with Bayesian and Akaike Information Criterion for Realistic Field Strength Attenuation Estimation in Open and Shadow urban Microcells”, Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) vol. 10 (4), pp.151-156, 2019.

[32]Sanjiv Kumar, Pradeep Gupta, Ghanshyam Singh and  Chauhan, D.S, "Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation“, International Journal of Intelligent Systems Technologies and Applications 05(9):94-102, 2013. DOI: 10.5815/ijisa.2013.09.11