Open AccessBi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system
Author(s) -
Xinyu Liu,
Yongjun Wang,
Xishuo Wang,
Hui Xu,
Chao Li,
Xiangjun Xin
Publication year - 2021
Publication title -
optics express
Language(s) - Danish
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.416672
Subject(s) - quadrature amplitude modulation , forward error correction , computer science , qam , optical communication , optics , artificial neural network , modulation (music) , dbm , electronic engineering , transmission (telecommunications) , optical power , bit error rate , physics , telecommunications , bandwidth (computing) , engineering , artificial intelligence , decoding methods , amplifier , acoustics , laser
We propose a bi-directional gated recurrent unit neural network based nonlinear equalizer (bi-GRU NLE) for coherent optical communication systems. The performance of bi-GRU NLE has been experimentally demonstrated in a 120 Gb/s 64-quadrature amplitude modulation (64-QAM) coherent optical communication system with a transmission distance of 375 km. Experimental results show that the proposed bi-GRU NLE can significantly mitigate nonlinear distortions. The Q-factors can exceed the hard-decision forward error correction (HD-FEC) limit of 8.52 dB with the aid of bi-GRU NLE, when the launched optical power is in the range of -3 dBm to 3 dBm. In addition, when the launched optical power is in the range of 0 dBm to 2 dBm, the Q-factor performances of the bi-GRU NLE and bi-directional long short-term memory neural network based nonlinear equalizer (bi-LSTM NLE) are similar, while the number of parameters of bi-GRU NLE is about 20.2% less than that of bi-LSTM NLE, the average training time of bi-GRU NLE is shorter than that of bi-LSTM NLE, the number of multiplications required for the bi-GRU NLE to equalize per symbol is about 24.5% less than that for bi-LSTM NLE.