Uncovering latency anomalies in 5G RAN - A combination learner approach
Author:
Affiliation:
1. Umeå University,Department of Computing Science,Umeå,Sweden
Funder
Knut and Alice Wallenberg Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9668311/9667790/09668431.pdf?arnumber=9668431
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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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
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