Adaptive root cause analysis for self-healing in 5G networks

Harrison Mfula, Jukka K. Nurminen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

Root cause analysis (RCA) is a common and recurring task performed by operators of cellular networks. It is done mainly to keep customers satisfied with the quality of offered services and to maximize return on investment (ROI) by minimizing and where possible eliminating the root causes of faults in cellular networks. Currently, the actual detection and diagnosis of faults or potential faults is still a manual and slow process often carried out by network experts who manually analyze and correlate various pieces of network data such as, alarms, call traces, configuration management (CM) and key performance indicator (KPI) data in order to come up with the most probable root cause of a given network fault. In this paper, we propose an automated fault detection and diagnosis solution called adaptive root cause analysis (ARCA). The solution uses measurements and other network data together with Bayesian network theory to perform automated evidence based RCA. Compared to the current common practice, our solution is faster due to automation of the entire RCA process. The solution is also cheaper because it needs fewer or no personnel in order to operate and it improves efficiency through domain knowledge reuse during adaptive learning. As it uses a probabilistic Bayesian classifier, it can work with incomplete data and it can handle large datasets with complex probability combinations. Experimental results from stratified synthesized data affirmatively validate the feasibility of using such a solution as a key part of self-healing (SH) especially in emerging self-organizing network (SON) based solutions in LTE Advanced (LTE-A) and 5G.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017
EditorsWaleed W. Smari
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages136-143
Number of pages8
ISBN (Electronic)9781538632505
ISBN (Print)9781538632505
DOIs
Publication statusPublished - 12 Sep 2017
MoE publication typeA4 Article in a conference publication

Fingerprint

Roots
Fault
Cellular Networks
Fault Detection and Diagnosis
LTE-advanced
Circuit theory
Bayesian networks
Bayesian Classifier
Performance Indicators
Adaptive Learning
Fault detection
Incomplete Data
Failure analysis
Domain Knowledge
Self-organizing
Probable
Bayesian Networks
Quality of service
Large Data Sets
Classifiers

Keywords

  • 5G
  • LTE-A
  • Root cause analysis
  • Self-healing

Cite this

Mfula, H., & Nurminen, J. K. (2017). Adaptive root cause analysis for self-healing in 5G networks. In W. W. Smari (Ed.), Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017 (pp. 136-143). [8035070] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPCS.2017.31
Mfula, Harrison ; Nurminen, Jukka K. / Adaptive root cause analysis for self-healing in 5G networks. Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. editor / Waleed W. Smari. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 136-143
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Mfula, H & Nurminen, JK 2017, Adaptive root cause analysis for self-healing in 5G networks. in WW Smari (ed.), Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017., 8035070, Institute of Electrical and Electronics Engineers Inc., pp. 136-143. https://doi.org/10.1109/HPCS.2017.31

Adaptive root cause analysis for self-healing in 5G networks. / Mfula, Harrison; Nurminen, Jukka K.

Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. ed. / Waleed W. Smari. Institute of Electrical and Electronics Engineers Inc., 2017. p. 136-143 8035070.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Mfula H, Nurminen JK. Adaptive root cause analysis for self-healing in 5G networks. In Smari WW, editor, Proceedings - 2017 International Conference on High Performance Computing and Simulation, HPCS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 136-143. 8035070 https://doi.org/10.1109/HPCS.2017.31