Abstract

We present a convolutional neural network architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution in both distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54, and 0.64 dB) and standard deviation set (0.62, 0.43, and 0.38 dB) of the maximum test error are obtained numerically employing two and three counter-, and four bidirectional propagating pumps, respectively.

© 2021 Optical Society of America

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

Uiara Celine de Moura, Ann Margareth Rosa Brusin, Andrea Carena, Darko Zibar, and Francesco Da Ros
Opt. Lett. 46(5) 1157-1160 (2021)

Inverse design of plasmonic metasurfaces by convolutional neural network

Ronghui Lin, Yanfen Zhai, Chenxin Xiong, and Xiaohang Li
Opt. Lett. 45(6) 1362-1365 (2020)

Color polarization demosaicking by a convolutional neural network

Yuanyuan Sun, Junchao Zhang, and Rongguang Liang
Opt. Lett. 46(17) 4338-4341 (2021)

Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks

Xiuting Zou, Shaofu Xu, Shujing Li, Jianping Chen, and Weiwen Zou
Opt. Lett. 44(23) 5723-5726 (2019)

Broadband achromatic metalens design based on deep neural networks

Xipeng An, Yue Cao, Yunxuan Wei, Zhihao Zhou, Tie Hu, Xing Feng, Guangqiang He, Ming Zhao, and Zhenyu Yang
Opt. Lett. 46(16) 3881-3884 (2021)