首页|Fast mode decomposition for few-mode fiber based on lightweight neural network
Fast mode decomposition for few-mode fiber based on lightweight neural network
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In this paper,we present a fast mode decomposition method for few-mode fibers,utilizing a lightweight neural network called MobileNetV3-Light.This method can quickly and accurately predict the amplitude and phase information of different modes,enabling us to fully characterize the optical field without the need for expensive experimental equipment.We train the MobileNetV3-Light using simulated near-field optical field maps,and evaluate its performance using both simulated and reconstructed near-field optical field maps.To validate the effectiveness of this method,we conduct mode decomposition experiments on a few-mode fiber supporting six linear polarization[LP]modes[LP01,LP11e,LP11o,LP21e,LP21o,LP02].The results demonstrate a remarkable average correlation of 0.9995 between our simulated and reconstructed near-field light-field maps.And the mode decomposition speed is about 6 ms per frame,indicating its powerful real-time processing capability.In addition,the proposed network model is compact,with a size of only 6.5 MB,making it well suited for deploy-ment on portable mobile devices.
deep learninglightweight neural networkfew-mode fibermode decomposition
赵佳佳、陈国辉、毕轩、蔡汪洋、岳磊、唐明
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School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China
Wuhan National Laboratory for Optoelectronics[WNLO]and National Engineering Laboratory for Next Generation Internet Access System,School of Optical and Electronic Information,Huazhong University of Science and Technology,Wuhan 430074,China
Scientific Research Fund of Hunan Provincial Education Department of China湖南省自然科学基金
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