Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets
With the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks. Developing effective self-organizing approaches often dep...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10869477/ |
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| author | Hsin-Chang Tsai Ming-Chun Lee Chao-Hao Hsu |
| author_facet | Hsin-Chang Tsai Ming-Chun Lee Chao-Hao Hsu |
| author_sort | Hsin-Chang Tsai |
| collection | DOAJ |
| description | With the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks. Developing effective self-organizing approaches often depends on data-driven or learning-based methods, which require well-structured and balanced datasets. However, in practical scenarios, datasets are often imbalanced or even very small. To address this issue from the fault diagnosis aspect of SONs, this paper investigates the learning-based fault and severity diagnosis approaches under imbalanced and small datasets for wireless networks. We first propose a deep learning-based diagnosis framework, in which the diagnosis problem can be cast as a regression problem. Then, several approaches, including re-weighting, distribution smoothing, and balanced MSE, that can be used to resolve the imbalanced issue for regression problem are examined under the diagnosis purpose. Subsequently, to resolve the issue that the amount of data samples for diagnosis could be few, model pre-training and meta-learning-based approaches are used, aiming to quickly adapt the pre-trained diagnosis model to the targeting scenarios for diagnosis. Extensive simulation results based on realistic setups are conducted to evaluate our proposed approaches. Results show that our approaches can effectively diagnose the faults and their severity and outperform the baseline approaches under imbalanced and small datasets. |
| format | Article |
| id | doaj-art-24acbb2a634a4e81a22accb3e4be8c65 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-24acbb2a634a4e81a22accb3e4be8c652025-02-11T00:01:27ZengIEEEIEEE Access2169-35362025-01-0113235082352510.1109/ACCESS.2025.353765910869477Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small DatasetsHsin-Chang Tsai0https://orcid.org/0009-0007-0041-1829Ming-Chun Lee1https://orcid.org/0000-0002-3493-3998Chao-Hao Hsu2https://orcid.org/0009-0002-3683-7120Institute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanInstitute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanInstitute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanWith the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks. Developing effective self-organizing approaches often depends on data-driven or learning-based methods, which require well-structured and balanced datasets. However, in practical scenarios, datasets are often imbalanced or even very small. To address this issue from the fault diagnosis aspect of SONs, this paper investigates the learning-based fault and severity diagnosis approaches under imbalanced and small datasets for wireless networks. We first propose a deep learning-based diagnosis framework, in which the diagnosis problem can be cast as a regression problem. Then, several approaches, including re-weighting, distribution smoothing, and balanced MSE, that can be used to resolve the imbalanced issue for regression problem are examined under the diagnosis purpose. Subsequently, to resolve the issue that the amount of data samples for diagnosis could be few, model pre-training and meta-learning-based approaches are used, aiming to quickly adapt the pre-trained diagnosis model to the targeting scenarios for diagnosis. Extensive simulation results based on realistic setups are conducted to evaluate our proposed approaches. Results show that our approaches can effectively diagnose the faults and their severity and outperform the baseline approaches under imbalanced and small datasets.https://ieeexplore.ieee.org/document/10869477/Self-organizing networksfault and severity diagnosisdeep-learningimbalanced regressionmeta-learning |
| spellingShingle | Hsin-Chang Tsai Ming-Chun Lee Chao-Hao Hsu Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets IEEE Access Self-organizing networks fault and severity diagnosis deep-learning imbalanced regression meta-learning |
| title | Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets |
| title_full | Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets |
| title_fullStr | Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets |
| title_full_unstemmed | Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets |
| title_short | Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets |
| title_sort | fault and severity diagnosis using deep learning for self organizing networks with imbalanced and small datasets |
| topic | Self-organizing networks fault and severity diagnosis deep-learning imbalanced regression meta-learning |
| url | https://ieeexplore.ieee.org/document/10869477/ |
| work_keys_str_mv | AT hsinchangtsai faultandseveritydiagnosisusingdeeplearningforselforganizingnetworkswithimbalancedandsmalldatasets AT mingchunlee faultandseveritydiagnosisusingdeeplearningforselforganizingnetworkswithimbalancedandsmalldatasets AT chaohaohsu faultandseveritydiagnosisusingdeeplearningforselforganizingnetworkswithimbalancedandsmalldatasets |