Modeling and Simulation of GSM/GPRS Network Derived Error-log Messages Summary Using Bayesian Inference Technique (A case study of Airtel Nig. Networks)

  IJPTT-book-cover
 
International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2015 by IJPTT Journal
Volume - 5 Issue - 1
Year of Publication : 2015
Authors : Otori A. U , Adetiba O.E , Dajab D. D , Muazu M. B
  10.14445/22492615/IJPTT-V15P402

MLA

Otori A. U , Adetiba O.E , Dajab D. D , Muazu M. B "Modeling and Simulation of GSM/GPRS Network Derived Error-log Messages Summary Using Bayesian Inference Technique (A case study of Airtel Nig. Networks) ". International Journal of P2P Network Trends and Technology (IJPTT), V5(1): 8-14 Jan - Feb 2015, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

One of the most important requirements to be addressed by a general purpose Fault Management (FM) system is the ability to quickly identify the root cause of network errors and fix them as soon as possible. This informs the maintenance of an accurate model of the mobile network error logs for the FM task. Filtering and Correlation are two methods we used to simplify the separation of the principal alarms and redundant alarms from their side effect on network performance. An algorithm: Bayesian Inference Technique provides a platform to systematically combine the qualitative and quantitative aspects of the Bayesian model for network fault management analysis and to reduce total computational complexity by providing a database of software alarm parameters respectively. These resulted in a Global Bayesian Network which helps to represent causal chains, i.e. links between cause/effect relationships to provide the evidence of past events and predict the most likely future causes and their symptoms by computing Conditional Probabilities of each Symptom. This is critical and useful for effective Global Systems for Mobile Communications/General Packet Radio Service (GSM/GPRS) Fault Management Systems by reducing the total downtime translsting into improved quality of service (QoS).

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Keywords
Fault Management; Filtering and Correlation; Bayesian Inference .