Supervised and Semi-Supervised Learning for Failure Identification in Microwave Networks

• Published in: IEEE eTransactions on network and service management Vol. 18; no. 2; pp. 1934 - 1945
• Main Authors: Musumeci, Francesco, Magni, Luca, Ayoub, Omran, Rubino, Roberto, Capacchione, Massimiliano, Rigamonti, Gabriele, Milano, Michele, Passera, Claudio, Tornatore, Massimo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Automation; Availability; Communication networks; data augmentation; Domains; Failure; failure identification; Identification; Inspection; Labeling; Labelling; Machine learning; Management systems; Manuals; Microwave amplifiers; Microwave antennas; Microwave communication; Microwave networks; Microwave theory and techniques; Neural networks; Parameter identification; Receiving antennas; Reconfiguration; root-cause analysis; Semi-supervised learning; Subject specialists; Support vector machines; Troubleshooting

Automated failure-cause identification in communication networks allows operators to reduce service unavailability. Once the most likely failure root-cause is identified, appropriate countermeasures can be effectively put in place (e.g., by choosing an in-field intervention vs. a remote equipment re...