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.
Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links / Andrenacci, I.; Lonardi, M.; Ramantanis, P.; Awwad, E.; Irurozki, E.; Clemencon, S.; Serena, P.; Lasagni, C.; Bigo, S.; Layec, P.. - (2024). (Intervento presentato al convegno 2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 tenutosi a usa nel 2024).
Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links
Lonardi M.;Serena P.;Lasagni C.;
2024-01-01
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.File in questo prodotto:
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