Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

ABSTRACT 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. 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. Keywords: Passive optical networks, fault monitoring, machine learning, optical time domain reflectometry 1. INTRODUCTION Passive optical networks (PONs) have gained popularity as a broadband fiber access network solution due to their service transparency, cost effectiveness, and scalability among other benefits [1].