Adaptive stochastic parallel gradient descent approach for efficient fiber coupling
In high-speed free-space optical communication systems, the received laser beam must be coupled into a single-mode fiber at the input of the receiver module. However, propagation through atmospheric turbulence degrades the spatial coherence of a laser beam and poses challenges for fiber coupling. In this paper, we propose a novel method, called as adaptive stochastic parallel gradient descent (ASPGD), to achieve efficient fiber coupling. To be specific, we formulate the fiber coupling problem as a model-free optimization problem and solve it using ASPGD in parallel. To avoid converging to the extremum points and accelerate its convergence speed, we integrate the momentum and the adaptive gain coefficient estimation to the original stochastic parallel gradient descent (SPGD) method. Simulation and experimental results demonstrate that the proposed method reduces 50% of iterations, while keeping the stability by comparing it with the original SPGD method.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2020 |
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Erschienen: |
2020 |
Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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Enthalten in: |
Optics express - 28(2020), 9 vom: 27. Apr., Seite 13141-13154 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Hu, Qintao [VerfasserIn] |
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Links: |
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Themen: |
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Anmerkungen: |
Date Revised 14.05.2020 published: Print Citation Status PubMed-not-MEDLINE |
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doi: |
10.1364/OE.390762 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM309867614 |
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520 | |a In high-speed free-space optical communication systems, the received laser beam must be coupled into a single-mode fiber at the input of the receiver module. However, propagation through atmospheric turbulence degrades the spatial coherence of a laser beam and poses challenges for fiber coupling. In this paper, we propose a novel method, called as adaptive stochastic parallel gradient descent (ASPGD), to achieve efficient fiber coupling. To be specific, we formulate the fiber coupling problem as a model-free optimization problem and solve it using ASPGD in parallel. To avoid converging to the extremum points and accelerate its convergence speed, we integrate the momentum and the adaptive gain coefficient estimation to the original stochastic parallel gradient descent (SPGD) method. Simulation and experimental results demonstrate that the proposed method reduces 50% of iterations, while keeping the stability by comparing it with the original SPGD method | ||
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700 | 1 | |a Mao, Yao |e verfasserin |4 aut | |
700 | 1 | |a Zhu, Shiwei |e verfasserin |4 aut | |
700 | 1 | |a Zhou, Xi |e verfasserin |4 aut | |
700 | 1 | |a Zhou, Guozhong |e verfasserin |4 aut | |
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