RT Journal Article
T1 Data mining for fuzzy diagnosis systems in LTE networks.
A1 Khatib, Emil Jatib
A1 Barco-Moreno, Raquel
A1 Gómez-Andrades, Ana
A1 Muñoz, Pablo
A1 Serrano, Inmaculada
K1 Sistemas difusos
K1 Minería de datos
K1 Telecomunicaciones
AB The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks.
PB Elsevier
YR 2015
FD 2015-06-01
LK https://hdl.handle.net/10630/34360
UL https://hdl.handle.net/10630/34360
LA eng
NO Khatib, E. J., Barco, R., Gómez-Andrades, A., Muñoz, P., & Serrano, I. (2015). Data mining for fuzzy diagnosis systems in LTE networks. Expert Systems with Applications, 42(21), 7549–7559.
NO Optimi-Ericsson, Junta de Andalucía (Consejería de Ciencia, Innovación y Empresa, Agencia IDEA, Junta de Andalucía Ref. 59288; Proyecto de Investigación de Excelencia P12-TIC-2905), ERDF.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026