Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links.

Isaia Andrenacci,Matteo Lonardi,Petros Ramantanis,Elie Awwad,Ekhiñe Irurozki, Stéphan Clémençon,Paolo Serena,Chiara Lasagni,Sébastien Bigo,Patricia Layec

Optical Fiber Communications Conference and Exhibition（2024）

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摘要

We propose a strategy to dynamically adjust transmitted power solely based on the analysis of performance fluctuations due to polarization-dependent loss. We show that our method converges faster to optimum compared to a standard approach.

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关键词

Optical Power,Point-to-point Links,Random Forest,Probability Density Function,Input Features,Machine Learning Classifiers,Input Power,Power Range,Average Signal-to-noise Ratio,Input Feature Vector,Power Adjustment,Signal-to-noise Ratio Improvement,Standard Single-mode Fiber,Amplified Spontaneous Emission,Power Corrections,Nonlinear Threshold,Mid Range,Macro F1 Score

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