TY - JOUR
T1 - Convolutional Neural Network-Aided DP-64 QAM Coherent Optical Communication Systems
AU - Li, Chao
AU - Wang, Yongjun
AU - Wang, Jingjing
AU - Yao, Haipeng
AU - Liu, Xinyu
AU - Gao, Ran
AU - Yang, Leijing
AU - Xu, Hui
AU - Zhang, Qi
AU - Ma, Pengjie
AU - Xin, Xiangjun
N1 - Publisher Copyright:
© 1983-2012 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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$\%$.
AB - 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$\%$.
KW - Optical fiber nonlinearity compensation
KW - convolutional neural network
KW - nonlinear signal distortion
KW - perturbation-based nonlinearity compensation
UR - http://www.scopus.com/inward/record.url?scp=85124099052&partnerID=8YFLogxK
U2 - 10.1109/JLT.2022.3146839
DO - 10.1109/JLT.2022.3146839
M3 - Article
AN - SCOPUS:85124099052
SN - 0733-8724
VL - 40
SP - 2880
EP - 2889
JO - Journal of Lightwave Technology
JF - Journal of Lightwave Technology
IS - 9
ER -