Nonlinear neural network equalizer for metro optical fiber communication systems
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We present a neural network-based nonlinear electronic feed-forward equalizer. It compensates for the chromatic dispersion (CD) distortions in fiber optic communication systems with direct photo-detection. The proposed equalizer achieves bit error rate (BER) performance comparable to the maximum-likelihood sequence estimator (MLSE), with significantly lower computational cost. The complexity of the introduced equalizer scales linearly with the length of the inter-symbol interference (ISI) as opposed to exponential growth the MLSE complexity.
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