Reduced Complexity Nonlinearity Compensation via Principal Component Analysis and Deep Neural Networks

被引：0
|
作者：
Gao, Yuliang [1 ]
El-Sahn, Ziad A. [1 ]
Awadalla, Ahmed [1 ]
Yao, Demin [1 ]
Sun, Han [1 ]
Mertz, Pierre [2 ]
Wu, Kuang-Tsan [1 ]
机构：
[1] Infinera Canada, 555 Legget Dr, Kanata, ON K2K 2X3, Canada
[2] Infinera Corp, 9005 Junct Dr, Savage, MD 20763 USA
来源：
2019 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC) | 2019年
关键词：
D O I：
暂无
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 demonstrate a novel fiber nonlinearity post-equalization algorithm using principal component analysis and neural networks. We achieve similar to 0.46 dBQ improvement for 21 Gbaud DP-8QAM transmission over similar to 13,000 km deployed fiber with over 90% complexity reduction.
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页数：3
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