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Maximum-Likelihood Symbol Detection by Dummy-assisted Low-complexity ANN for PAM-4 Transmission

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Abstract

An artificial neural network (ANN) based maximum-likelihood (ML) symbol detection is proposed for nonlinearity compensation in PAM-4. Its optimized architecture realizes 0.7-dB better sensitivity and 20-times lower complexity than the conventional ANN-based ML sequence detection.

© 2018 The Author(s)

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