Title
Automatic diagnosis of mobile communication networks under imprecise parameters
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.
Year
DOI
Venue
2009
10.1016/j.eswa.2007.09.030
Expert Syst. Appl.
Keywords
Field
DocType
automatic diagnosis,cellular system,model parameter,wireless networks,fault management,self-healing,cellular network,probabilistic reasoning,gprs network,mobile communication network,network management,bayesian networks,diagnosis,network operation,automated management,imprecise parameter,diagnosis accuracy,discrete bayesian network,radio access network,proposed technique,mobile communications,expert systems,traditional bns,troubleshooting,self-optimizing networks,self organization,expert system,wireless network,self optimizing networks,bayesian network,empirical research,mobile communication
Wireless network,Data mining,Computer science,Self-organizing network,Fault management,Bayesian network,Cellular network,Artificial intelligence,Probabilistic logic,Network management,Mobile telephony,Machine learning
Journal
Volume
Issue
ISSN
36
1
Expert Systems With Applications
Citations 
PageRank 
References 
13
0.98
18
Authors
4
Name
Order
Citations
PageRank
Raquel Barco136441.12
L. Díez214123.21
Volker Wille313013.37
Pedro Lázaro4130.98