Automatic diagnosis of mobile communication networks under imprecise parameters

Authors:
Raquel Barco;Luis Díez;Volker Wille;Pedro Lázaro
Affiliations:
Departamento Ingeniería de Comunicaciones, ETSI Telecomunicación, University of Málaga, E-29071 Málaga, Spain;Departamento Ingeniería de Comunicaciones, ETSI Telecomunicación, University of Málaga, E-29071 Málaga, Spain;Nokia Siemens Networks, Consulting & Systems Integration, Ermine Business Park, Huntingdon, Cambridgeshire, PE29 6YJ, UK;Departamento Ingeniería de Comunicaciones, ETSI Telecomunicación, University of Málaga, E-29071 Málaga, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 11
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Decision-theoretic troubleshooting

Communications of the ACM
Schemes for fault identification in communication networks

IEEE/ACM Transactions on Networking (TON)
The sensitivity of belief networks to imprecise probabilities: an experimental investigation

Artificial Intelligence - Special volume on empirical methods
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Machine Learning - Special issue on learning with probabilistic representations
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Model-Based Alarm Correlation in Cellular Phone Networks

MASCOTS '97 Proceedings of the 5th International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
The SACSO methodology for troubleshooting complex systems

Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Probabilistic fault localization in communication systems using belief networks

IEEE/ACM Transactions on Networking (TON)
Probability and Statistics for Computer Science

Probability and Statistics for Computer Science
Sensitivity analysis in discrete Bayesian networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

A framework for development of integrated intelligent knowledge for management of telecommunication networks

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.07

Visualization

Abstract

In the last years, self-organization of cellular networks is becoming a crucial aspect of network management due to the increasing complexity of the networks. Automatic fault identification, i.e. diagnosis, is the most difficult task in self-healing. In this paper, a model based on discrete bayesian networks (BNs) is proposed for diagnosis of radio access networks of cellular systems. Normally, inaccuracies are unavoidable in the parameters of the model (interval limits for discretized symptoms and probabilities in the BN). In order to enhance the performance of BNs, a methodology to model the ''continuity'' in the human reasoning is presented, named smooth bayesian networks (SBNs). SBNs are intended to decrease the sensitivity of diagnosis accuracy to imprecision in the definition of the model parameters. An empirical research campaign has been carried out in a live GSM/GPRS network in order to assess the performance of the proposed techniques. Results have shown that SBNs outperform traditional BNs when there is inaccuracy in the model parameters.