Accelerating TDECQ Assessments using Convolutional Neural Networks

被引：0

作者：

Varughese, Siddharth ^{[1
]}

Garon, Daniel A. ^{[1
]}

Melgar, Alirio ^{[1
]}

Thomas, Varghese A. ^{[1
]}

Zivny, Pavel ^{[2
]}

Hazzard, Shane ^{[2
]}

Ralph, Stephan E. ^{[1
]}

机构：

[1] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

[2] Tektronix Inc, Beaverton, OR 97007 USA

来源：

2020 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC) | 2020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 experimentally demonstrate the use of convolutional neural networks to accelerate TDECQ assessments for 400G direct-detect transmitter qualification. The method estimates TDECQ from static eye-diagrams similar to 1000 times faster than conventional methods with <0.25dB mean discrepancy.

引用

页数：3

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