Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

TL;DR

This paper explores machine learning techniques to improve fault detection in passive optical networks, addressing challenges like overlapping signals and increasing network complexity, validated through experimental data.

Contribution

It introduces ML-based methods for fault monitoring in PON systems and validates them with real OTDR data, enhancing reliability in complex networks.

Findings

ML approaches effectively detect faults in PON systems

Experimental validation confirms improved fault identification accuracy

Addresses challenges of overlapping signals and network complexity

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. 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.