Optical fiber
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Machine Learning Based Optical Fiber Fault Detection and Localization Using OTDR Signals
Abstract: Optical fiber networks provide high bandwidth, low transmission loss, and immunity to electromagnetic interference, making them fundamental to modern communication infrastructure. However, fiber faults caused by cable cuts, excessive bending, connector degradation, splicing defects, and physical disturbances can significantly affect network reliability. Optical time-domain reflectometry (OTDR) is widely used for identifying and locating such faults, but conventional OTDR interpretation often depends on threshold-based signal processing and manual analysis. This study …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 41–52 Read article
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Machine Learning Based Optimization of Optical Fiber Parameters for Minimizing Dispersion and Attenuation
Abstract: Optical fibers provide high-bandwidth and low-loss transmission for modern communication networks; however, attenuation and chromatic dispersion remain two important physical limitations affecting transmission distance, pulse integrity, and system capacity. Conventional optical-fiber design generally relies on analytical calculations and repeated numerical simulations to determine suitable geometric and material parameters. Such approaches become computationally demanding when several fiber parameters are simultaneously optimized. This paper presents a machine-learning-based methodology for predicting and optimizing …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 18–28 Read article