Fault Monitoring in Passive Optical Networks using Machine Learning Techniques
This addresses the problem of unreliable fault monitoring for service providers in PON systems, which is incremental as it applies existing ML techniques to a specific domain.
The paper tackles fault monitoring in passive optical networks (PONs) by proposing machine learning approaches to identify faulty optical network units (ONUs), validated using experimental optical time domain reflectometry (OTDR) data.
Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interruption caused by a fiber cut can result in huge financial losses for service providers or operators. Identifying the faulty ONU becomes difficult in the case of nearly equidistant branch terminations because the reflections from the branches overlap, making it difficult to distinguish the faulty branch given the global backscattering signal. With increasing network size, the complexity of fault monitoring in PON systems increases, resulting in less reliable monitoring. To address these challenges, we propose in this paper various machine learning (ML) approaches for fault monitoring in PON systems, and we validate them using experimental optical time domain reflectometry (OTDR) data.