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Fast BER Distribution and Neural Networks for Joint Monitoring of Linear and Nonlinear Noise-to-Signal Ratios

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Abstract

Experimentally observed long-tail fast BER (10ns–1µs) histogram (FBH) in presence of NLIN is explained through simulation. Features from FBHs are applied to train an ANN to estimate linear and nonlinear NSRs with <5% error.

© 2020 The Author(s)

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