Abstract

One of the most promising solutions to overcome the capacity limit of current optical fiber links is space-division multiplexing, which allows the transmission on various cores of multi-core fibers or modes of few-mode fibers. In order to realize such systems, suitable optical fiber amplifiers must be designed. In single mode fibers, Raman amplification has shown significant advantages over doped fiber amplifiers due to its low-noise and spectral flexibility. For these reasons, its use in next-generation space-division multiplexing transmission systems is being studied extensively. In this work, we propose a deep learning method that uses automatic differentiation to embed a complete few-mode Raman amplification model in the training process of a neural network to identify the optimal pump wavelengths and power allocation scheme to design both flat and tilted gain profiles. Compared to other machine learning methods, the proposed technique allows to train the neural network on ideal gain profiles, removing the need to compute a dataset that accurately covers the space of Raman gains we are interested in. The ability to directly target a selected region of the space of possible gains allows the method to be easily generalized to any type of Raman gain profiles, while also being more robust when increasing the number of pumps, modes, and the amplification bandwidth. This approach is tested on a 70 km long 4-mode fiber transmitting over the C+L band with various numbers of Raman pumps in the counter-propagating scheme, targeting gain profiles with an average gain in the interval from 5 dB to 15 dB and total tilt in the interval from $-$ 1.425 to 1.425 dB. We achieve wavelength- and mode-dependent gain fluctuations lower than 0.04 dB and 0.02 dB per dB of gain, respectively.

Intelligent gain flattening in wavelength and space domain for FMF Raman amplification by machine learning based inverse design

Yufeng Chen, Jiangbing Du, Yuting Huang, Ke Xu, and Zuyuan He
Opt. Express 28(8) 11911-11920 (2020)

Recognition of the orbital-angular-momentum spectrum for hybrid modes existing in a few-mode fiber via a deep learning method

Hua Zhao, Jiannan Xu, Yuanyuan Hao, Jiayang Xu, Huali Lu, Hui Hao, Ting Zhao, Pengfei Li, Peng Wang, and Hongpu Li
Opt. Express 31(19) 30627-30638 (2023)

Learning to decompose the modes in few-mode fibers with deep convolutional neural network

Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, and Pu Zhou
Opt. Express 27(7) 10127-10137 (2019)

Few-mode fiber design for multiple-input-multiple-output-less mode division multiplexing by machine learning

Yudan Cheng, Yinghao Guo, Min Cao, Youchao Jiang, Wenhua Ren, and Guobin Ren
J. Opt. Soc. Am. B 39(9) 2421-2428 (2022)

Dual-pump few-mode fiber optical parametric amplification: analytical model and optimization strategy

Xiaoshan Huang, Meng Xiang, Songnian Fu, Jianping Li, and Yuwen Qin
J. Opt. Soc. Am. B 39(11) 3088-3099 (2022)