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Accelerating TDECQ Assessments using Convolutional Neural Networks

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Abstract

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 ~1000 times faster than conventional methods with <0.25dB mean discrepancy.

© 2020 The Author(s)

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