End-to-end Learning for Fiber Nonlinearity Mitigation Geometric Shaping via RNN-based Autoencoder

被引：0
|
作者：
Liu, Zhiyang [1 ]
Chen, Cao [1 ]
Xiao, Shilin [1 ]
Hu, Weisheng [1 ]
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Adv Opt Commun Syst & Networks, Shanghai, Peoples R China
来源：
2022 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE, ACP | 2022年
关键词：
geometric constellation shaping; recurrent neural network; nonlinearity mitigation;
D O I：
10.1109/ACP55869.2022.10088800
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We propose a novel scheme for fiber nonlinearity mitigation based on geometric constellation shaping using RNN-based end-to-end autoencoder structure. Simulation results show a Q-factor gain of 0.65 dB and the transmission distance extension compared to the unshaped constellation schemes at the optimal launch power.
引用
收藏
页码：928 / 930
页数：3
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