Fault prediction and reliability analysis in a real cellular network
Abstract
Today, the importance of cellular networks is ever-growing. The increasing complexity of networks is expected to decrease reliability. In order to continue reliable operation in a cost-efficient manner, previous literature has explored Proactive Self-Healing methods, but actual application to cellular networks has been lacking. Thus, in this paper, we aim to institute a proactive approach for failure prediction of time series data by surveying a wide range of techniques. To determine the best in predicting network failures, Support Vector Machine (SVM) Regression and multiple Neural Network variants were utilized along with a Continuous Time Markov Chain (CTMC) analytical model to provide reliability analysis. All results are derived from actual network data. We conclude the pattern of these failures is most likely non-linear, and the most promising technique is a Deep Neural Network utilizing Autoencoders. The CTMC analysis demonstrates that current networks barely reside in a healthy state, so the goal is that this paper will lead to improvements, especially in Self-Organizing Networks (SON).
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Kumar et al. [255] show that DNN with autoencoders outperform other ML techniques, including autoregressive NN, linear and nonlinear SVM, and exponential and linear regression, for fault prediction.
“In a comparison of the different ML-based techniques for fault prediction, they show that DNN with autoencoders outperform other ML techniques, including autoregressive NN, linear and nonlinear SVM, and exponential and linear regression.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [255]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Kumar, Y., Farooq, H., & Imran, A.
- Affiliations
- University of Oklahoma - Tulsa (US)
- ORCID
- Farooq, Hasan
- Venue
- 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 1090-1095
- OpenAlex
- W2737883091
- Cited by
- 26
- References
- 11
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 2 · 2019: 5 · 2018: 3
- FWCI
- 6.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Wireless Communications and Mobile Computing Conference (IWCMC) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- network failure prediction, time series forecasting, support vector regression, artificial neural networks, autoencoder, continuous-time Markov chains, self-organizing networks
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