Two-stage artificial neural network-based burst-subcarrier joint equalization in nonlinear frequency division multiplexing systems

We propose an artificial neural network (ANN)-based scheme to improve the performance of nonlinear frequency division multiplexing (NFDM) optical transmission systems in both time and frequency domain. Through two-stage ANN equalization at the receiver side, time-domain distortions between adjacent...

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Bibliographic Details
Published inOptics letters Vol. 46; no. 7; p. 1700
Main Authors Chen, Xinyu, Ming, Hao, Li, Chenjia, He, Guangqiang, Zhang, Fan
Format Journal Article
LanguageEnglish
Published United States 01.04.2021
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Summary:We propose an artificial neural network (ANN)-based scheme to improve the performance of nonlinear frequency division multiplexing (NFDM) optical transmission systems in both time and frequency domain. Through two-stage ANN equalization at the receiver side, time-domain distortions between adjacent bursts and frequency-domain cross talk between neighboring subcarriers can be jointly mitigated. Burst ANN and Subcarrier ANN equalizers are characterized and validated by numerical simulations of a dual-polarization NFDM transmission system. Compared with the basic detection scheme, the proposed two-stage ANN achieves a Q-factor gain of 3.01 dB for an NFDM system with 256 Gb/s gross data rate transmitting over 960 km standard single-mode fiber (SSMF). The two-stage ANN approach offers an effective way to jointly equalize the signal in multiple dimensions.
ISSN:1539-4794
DOI:10.1364/OL.422195