@inproceedings{417ed8d2f8574f38a79e0b0bece085fe,
title = "Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links",
abstract = "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.",
author = "Isaia Andrenacci and Matteo Lonardi and Petros Ramantanis and {\'E}lie Awwad and Ekhi{\~n}e Irurozki and Stephan Cl{\'e}men{\c c}on and Paolo Serena and Chiara Lasagni and S{\'e}bastien Bigo and Patricia Layec",
note = "Publisher Copyright: {\textcopyright} 2024 OSA.; 2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 ; Conference date: 24-03-2024 Through 28-03-2024",
year = "2024",
month = jan,
day = "1",
language = "English",
series = "2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings",
}