FUNDAMENTAL PRINCIPLES OF AUTOMATING OPTICAL REFLECTOMETER ANALYSIS IN TMN TECHNOLOGY

Abstract

Fiber optic measurements are aimed at determining various line characteristics, such as attenuation, reflection, fiber uniformity, quality of welded and detachable joints, as well as localization of defects and damage. A number of specialized instruments are used to perform such measurements, among which the optical reflectometer plays a key role. The Optical Time Domain Reflectometer (OTDR) serves as a fundamental diagnostic tool for fiber-optic communication lines (FOCLs) and is utilized at all stages of their lifecycle, from installation to long-term operation. This study presents the main types of optical reflectograms observed during FOCL deployment and maintenance, highlighting both idealized signal states and typical distortions that can occur during measurement procedures and result interpretation. The causes of FOCL degradation are multifactorial, including both age-related and anthropogenic factors.

The Telecommunications Management Network (TMN) technology, as defined by ITU-T Recommendation M.3010, provides a standardized framework for managing communication networks. This paper demonstrates that integrating artificial intelligence (AI) within the TMN framework enables a shift from reactive network management — repairing faults after they occur — to proactive and autonomous control. The automation of OTDR data analysis aims to reduce or eliminate human intervention, ensure continuous 24/7 operation, and minimize reaction time to network events.

The results underline the potential of combining OTDR diagnostics with AI-driven analysis in TMN-managed networks, providing enhanced reliability, efficiency, and accuracy in the assessment of optical line integrity. Such approaches are essential for modern telecommunication networks, where timely detection and resolution of fiber faults directly impact service quality and operational costs.