RANGAN: GAN-empowered Anomaly Detection in 5G Cloud RAN
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912557963411456 |
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| author | Liao, Douglas Luo, Jiping Vevstad, Jens Pappas, Nikolaos |
| author_facet | Liao, Douglas Luo, Jiping Vevstad, Jens Pappas, Nikolaos |
| contents | Radio Access Network (RAN) systems are inherently complex, requiring continuous monitoring to prevent performance degradation and ensure optimal user experience. The RAN leverages numerous key performance indicators (KPIs) to evaluate system performance, generating vast amounts of data each second. This immense data volume can make troubleshooting and accurate diagnosis of performance anomalies more difficult. Furthermore, the highly dynamic nature of RAN performance demands adaptive methodologies capable of capturing temporal dependencies to detect anomalies reliably. In response to these challenges, we introduce \textbf{RANGAN}, an anomaly detection framework that integrates a Generative Adversarial Network (GAN) with a transformer architecture. To enhance the capability of capturing temporal dependencies within the data, RANGAN employs a sliding window approach during data preprocessing. We rigorously evaluated RANGAN using the publicly available RAN performance dataset from the Spotlight project \cite{sun-2024}. Experimental results demonstrate that RANGAN achieves promising detection accuracy, notably attaining an F1-score of up to $83\%$ in identifying network contention issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RANGAN: GAN-empowered Anomaly Detection in 5G Cloud RAN Liao, Douglas Luo, Jiping Vevstad, Jens Pappas, Nikolaos Networking and Internet Architecture Radio Access Network (RAN) systems are inherently complex, requiring continuous monitoring to prevent performance degradation and ensure optimal user experience. The RAN leverages numerous key performance indicators (KPIs) to evaluate system performance, generating vast amounts of data each second. This immense data volume can make troubleshooting and accurate diagnosis of performance anomalies more difficult. Furthermore, the highly dynamic nature of RAN performance demands adaptive methodologies capable of capturing temporal dependencies to detect anomalies reliably. In response to these challenges, we introduce \textbf{RANGAN}, an anomaly detection framework that integrates a Generative Adversarial Network (GAN) with a transformer architecture. To enhance the capability of capturing temporal dependencies within the data, RANGAN employs a sliding window approach during data preprocessing. We rigorously evaluated RANGAN using the publicly available RAN performance dataset from the Spotlight project \cite{sun-2024}. Experimental results demonstrate that RANGAN achieves promising detection accuracy, notably attaining an F1-score of up to $83\%$ in identifying network contention issues. |
| title | RANGAN: GAN-empowered Anomaly Detection in 5G Cloud RAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.20985 |