We propose a convolutional-recurrent channel equalizer and experimentally demonstrate 1dB Q-factor improvement both in single-channel and 96×WDM, DP-16QAM transmission over 450km of TWC fiber. The new equalizer outperforms previous NN-based approaches and a 3-steps-per-span DBP.

We propose a novel design of neural network for mitigating the fiber nonlinearity, employing a structure based on physical modelling. The neural network achieved nearly 5 times BER reduction in a field trial when transmitting WDM 200G DP-16QAM over a 612 km legacy link.

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