Self-healing is one of the main functionalities of Self-Organizing-Networks. Among self-healing functions, diagnosis or root cause analysis, consisting of identifying the fault cause in problematic cells, is one of the most complex tasks. Expert systems, such as Fuzzy Logic Controllers or Bayesian Networks, have been previously proposed to implement automatic diagnosis systems in the radio access segment of mobile communication networks. In order to achieve accurate results, these diagnosis systems should contain the knowledge of experienced LTE troubleshooting experts. However, these experts normally have neither the time nor the expertise in artificial intelligence to define the expert system. In this work, we propose a novel knowledge acquisition system that obtains this knowledge in the least possible intrusive way. The proposed method collects the Performance Indicators data from the relevant time intervals together with the expert’s diagnosis and uses them as inputs for a Data Mining algorithm to extract diagnosis rules.
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This work has been partially funded by Optimi-Ericsson, Junta de Andalucía (Agencia IDEA, Consejería de Ciencia, Innovación y Empresa, Ref. 59288 and Proyecto de Investigación de Excelencia P12-TIC-2905) and ERDF.
Conflict of interest
The authors declare that they have no competing interests.
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Khatib, E.J., Barco, R., Muñoz, P. et al. Knowledge Acquisition for Fault Management in LTE Networks. Wireless Pers Commun 95, 2895–2914 (2017). https://doi.org/10.1007/s11277-017-3969-x
- Knowledge acquisition
- Fault management