Automated Diagnosis for UMTS Networks Using Bayesian Network Approach
Abstract
This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.
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Khanafer et al. [237] correct faults are diagnosed in 88.1% of cases using a 3-fold cross-validation test in a real UMTS network.
“Using a 3-fold cross-validation test, the correct faults are diagnosed in 88.1% of the cases.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [237]
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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
- Khanafer, R. M., Solana, B., Triola, J., Barco, R., Moltsen, L., Altman, Z., & Lazaro, P.
- Affiliations
- Orange (France) (FR)
- Telefonica Research and Development (ES)
- Universidad de Málaga (ES)
- ORCID
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2008-07
- Volume
- 57
- Issue
- 4
- Pages
- 2451-2461
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2153849055
- Cited by
- 135
- References
- 19
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 7 · 2022: 10 · 2021: 9 · 2020: 11 · 2019: 10 · 2018: 7 · 2017: 9 · 2016: 11 · 2015: 17 · 2014: 8 · 2013: 9 · 2012: 7
- FWCI
- 6.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Vehicular Technology (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- automated diagnosis, UMTS networks, Bayesian networks, network troubleshooting, naive Bayesian classifier, fault diagnosis, key performance indicators, operation and maintenance, mobile operators, network simulator
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