Unsupervised Learning in Next-Generation Networks: Real-Time Performance Self-Diagnosis

• Published in: IEEE communications letters Vol. 25; no. 10; pp. 3330 - 3334
• Main Authors: Mismar, Faris B., Hoydis, Jakob
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: artificial intelligence; Cellular communication; Clustering; Clustering algorithms; edge computing; Fault detection; Frequency modulation; Machine learning; Next generation networking; Radio access networks; Real time; Real-time systems; Self-diagnosis; Training data; Unsupervised learning
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2021.3101058

This letter demonstrates the use of unsupervised machine learning to enable performance self-diagnosis of next-generation cellular networks. We propose two simplified applications of unsupervised learning that can enable real-time performance self-diagnosis on edge nodes such as the radio access network intelligent controller (RIC). The first application detects anomalous performance and finds its root cause of faults, configuration, or network procedure failures. The second application uses clustering to learn the relationship between two performance measures. Our proposed applications run in near-constant time complexity, making them, combined with subject-matter expertise validation, suitable real-time RIC applications for network diagnosis.