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Fiber Nonlinearity Equalization with Multi-Label Deep Learning Scalable to High-Order DP-QAM

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Abstract

We use deep neural network (DNN) to compensate for Kerr-induced nonlinearity in fiber-optic communications. The proposed DNN is scalable to high-order modulations by employing multi-label classification, achieving greater than 1.2 dB gain in nonlinear regimes.

© 2018 The Author(s)

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