Accurate, robust, and fast mode decomposition for few-mode optical fiber with deep neural network

Yi An; Liangjin Huang; Jun Li; Jinyong Leng; Lijia Yang; Pu Zhou

doi:10.1117/12.2523130

14 February 2019 Accurate, robust, and fast mode decomposition for few-mode optical fiber with deep neural network

Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, Pu Zhou

Proceedings Volume 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018); 110484Y (2019) https://doi.org/10.1117/12.2523130
Event: 17th International Conference on Optical Communications and Networks (ICOCN2018), 2018, Zhuhai, China

Abstract

We introduce deep learning technique to perform robust mode decomposition (MD) for few-mode optical fiber. Our goal is to learn a robust, fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including both of the modal amplitudes and phases. Taking a few-mode fiber which supports 3 linearly polarized modes into consideration, simulated near-field beam profiles with known mode coefficient labels are generated and fed into the convolutional neural network (CNN) to carry out the training procedure. Further, saturated patterns are added into the training samples to increase the robustness. When the network gets convergence, ordinary and saturated beam patterns are both utilized to perform MD with pre-trained CNN. The average correlation value of the input and reconstructed patterns can reach as high as 0.9994 and 0.9959 respectively for two cases. The consuming time of MD for one beam pattern is about 10ms. The results have shown that deep learning techniques highly favors the accurate, robust and fast MD for few-mode fiber.

Citation Download Citation

Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, and Pu Zhou "Accurate, robust, and fast mode decomposition for few-mode optical fiber with deep neural network", Proc. SPIE 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018), 110484Y (14 February 2019); https://doi.org/10.1117/12.2523130

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