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We experimentally demonstrate VCSEL+MMF nonlinear Digital Pre Distorters, optimized using Convolutional Neural Networks, for fulfilling the IEEE P802.3dbTM/D3.2 TDECQ requirements for net 100 Gb/s/λ optical transmitters.

In this paper, an algorithm for the estimation of the linear inter-channel crosstalk in a dense-WDM polarizationmultiplexed 16-QAM transmission scenario is proposed and demonstrated. The algorithm is based on the use of a feedforward neural network (FFNN) inside the coherent digital receiver. Two types of FFNNs were considered, the first based on a regression algorithm and the second based on a classification algorithm. Both FFNN algorithms are applied to features extracted from the histograms of the in-phase and quadrature components of the equalized digital samples. After a simulative investigation, the performance of the channel spacing estimation algorithms was experimentally validated in a 3x52 Gbaud 16-QAM WDM system scenario.

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