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). (2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 usa 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.
2024
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). (2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 usa 2024).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2988314
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact