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 net...