RT Journal Article
T1 Combination of multiple diagnosis systems in self-healing networks
A1 Palacios, David
A1 Khatib, Emil Jatib
A1 Barco-Moreno, Raquel
K1 Sistemas de comunicaciones móviles
K1 Comunicación-Análisis de red
AB The Self-Organizing Networks (SON) paradigm proposes a set of functions to automate network management in mobile communication networks. Within SON, the purpose of Self-Healing is to detect cells with service degradation, diagnose the fault cause that affects them, rapidly compensate the problem with the support of neighboring cells and repair the network by performing some recovery actions.The diagnosis phase can be designed as a classifier. In this context, hybrid ensembles of classifiers enhance the diagnosis performance of expert systems of different kinds by combining their outputs. In this paper, a novel scheme of hybrid ensemble of classifiers is proposed as a two-step procedure: a modeling stage of the baseline classifiers and an application stage, when the combination of partial diagnoses is actually performed. The use of statistical models of the baseline classifiers allows an immediate ensemble diagnosis without running and querying them individually, thus resulting in a very low computational cost in the execution stage.Results show that the performance of the proposed method compared to its standalone components is significantly better in terms of diagnosis error rate, using both simulated data and cases from a live LTE network. Furthermore, this method relies on concepts which are not linked to a particular mobile communication technology, allowing it to be applied either on well established cellular networks, like UMTS, or on recent and forthcoming technologies, like LTE-A and 5G.
PB Elsevier
YR 2016
FD 2016-07
LK https://hdl.handle.net/10630/34319
UL https://hdl.handle.net/10630/34319
LA eng
NO David Palacios, Emil J. Khatib, Raquel Barco, Combination of multiple diagnosis systems in Self-Healing networks, Expert Systems with Applications, Volume 64, 2016, Pages 56-68, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2016.07.030.
NO Optimi-Ericsson, Junta de Andalucía (Consejería de Ciencia, Innovación y Empresa, Ref. 59288 y Proyecto de Investigación de Excelencia P12-TIC-2905), ERDF.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026