Uncovering latency anomalies in 5G RAN - A combination learner approach

Author:

Sundqvist Tobias1,Bhuyan Monowar1,Elmroth Erik1

Affiliation:

1. Umeå University,Department of Computing Science,Umeå,Sweden

Funder

Knut and Alice Wallenberg Foundation

Publisher

IEEE

Reference23 articles.

1. Anomaly Detection from Log Files Using Unsupervised Deep Learning

2. Robust log-based anomaly detection on unstable log data

3. Boosted Ensemble Learning for Anomaly Detection in 5G RAN

4. A survey on anomaly detection methods for system log data;ajith;International Journal of Science and Research (IJSR),2019

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1. AnDet: ML-Based Anomaly Detection of UEs in a Multi-cell B5G Mobile Network for Improved QoS;2024 International Conference on Computing, Networking and Communications (ICNC);2024-02-19

2. Deep Autoencoder Design for RF Anomaly Detection in 5G O-RAN Near-RT RIC via xApps;2023 IEEE International Conference on Communications Workshops (ICC Workshops);2023-05-28

3. Bottleneck identification and failure prevention with procedural learning in 5G RAN;2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid);2023-05

4. Robust Procedural Learning for Anomaly Detection and Observability in 5G RAN;IEEE Transactions on Network and Service Management;2023

5. 5G shortcomings and Beyond-5G/6G requirements;2022 1st International Conference on 6G Networking (6GNet);2022-07-06

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