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Convolutional Neural Network-Aided DP-64 QAM Coherent Optical Communication Systems

Optical nonlinearity impairments have been a major obstacle for high-speed, long-haul and large-capacity optical transmission. In this paper, we propose a novel convolutional neural network (CNN)-based perturbative nonlinearity compensation approach in which we reconstruct a feature map with two channels that rely on first-order perturbation theory and build a classifier and a regressor as a nonlinear equalizer. We experimentally demonstrate the CNN equalizer in 375 km 120-Gbit/s dual-polarization 64-quadrature-amplitude modulation (64-QAM) coherent optical communication systems. We studied the influence of the dropout value and nonlinear activation function on the convergence of the CNN equalizer. We measured the bit-error-ratio (BER) performance with different launched optical powers. When the channel size is 11, the optimum BER for the CNN classifier is 0.0012 with 1 dBm, and for the CNN regressor, it is 0.0020 with 0 dBm; the BER can be lower than the 7 % hard decision-forward threshold of 0.0038 from −3 dBm to 3 dBm. When the channel size is 15, the BERs at −4 dBm, 4 dBm and 5 dBm can be lower than 0.0020. The network complexity is also analyzed in this paper. Compared with perturbative nonlinearity compensation using a fully connected neural network (2392-64-64), we can verify that the time complexity is reduced by about 25 % , while the space complexity is reduced by about 50 % .

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.9 May 1 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2880-2889

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORK
OPTICAL FIBER NONLINEARITY COMPENSATION
NONLINEAR SIGNAL DISTORTION
PERTURBATION-BASED NONLINEARITY COMPENSATION

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3146839

Pernyataan Tanggungjawab

Chao Li, Yongjun Wang, Jingjing Wang, Haipeng Yao, Xinyu Liu, Ran Gao, Leijing Yang, Hui Xu, Qi Zhang, Pengjie Ma, Xiangjun Xin

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