Open AccessAdaptive stochastic parallel gradient descent approach for efficient fiber coupling
Author(s) -
Qintao Hu,
Liangli Zhen,
Yao Mao,
Shiwen Zhu,
Xi Zhou,
Guixiang Zhou
Publication year - 2020
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.390762
Subject(s) - stochastic gradient descent , gradient descent , computer science , free space optical communication , adaptive optics , optics , coupling (piping) , control theory (sociology) , optical fiber , convergence (economics) , coherence (philosophical gambling strategy) , physics , optical communication , materials science , artificial neural network , control (management) , quantum mechanics , machine learning , artificial intelligence , economics , metallurgy , economic growth
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.