Topic modeling for anomaly detection in telecommunication networks

• Published in: Journal of ambient intelligence and humanized computing Vol. 14; no. 11; pp. 15085 - 15096
• Main Authors: Steinhauer, H. Joe, Helldin, Tove, Mathiason, Gunnar, Karlsson, Alexander
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Anomalies; Artificial Intelligence; Automation; Computational Intelligence; Data analysis; Data mining; Decision-making; Dirichlet problem; Engineering; Modelling; Operators; Original Research; Performance degradation; Radio equipment; Robotics and Automation; Skövde Artificial Intelligence Lab (SAIL); Telecommunication anomaly detection; Topic modeling; User Interfaces and Human Computer Interaction; Variables; Telecommunication anomaly detection; Topic modeling; Decision-making
• ISSN: 1868-5137; 1868-5145; 1868-5145
• DOI: 10.1007/s12652-019-01372-5

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.