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

被引：0

作者：

Andrenacci, Isaia ^{[1
,2
]}

Lonardi, Matteo ^{[2
]}

Ramantanis, Petros ^{[2
]}

Awwad, Elie ^{[1
]}

Irurozki, Ekhine ^{[1
]}

Clemencon, Stephan ^{[1
]}

Serena, Paolo ^{[3
]}

Lasagni, Chiara ^{[3
]}

Bigo, Sebastien ^{[2
]}

Layec, Patricia ^{[2
]}

机构：

[1] Telecom Paris, 19 Pl Marguerite Perey, F-91120 Palaiseau, France

[2] Nokia Bell Labs, 12 Rue Jean Bart, F-91300 Massy, France

[3] Univ Parma, Parco Area Sci 181-A, I-43124 Parma, Italy

来源：

2024 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION, OFC | 2024年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

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. (c) 2024 The Author(s)

引用

页数：3

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