Unsupervised Domain-Adaptation Based Nonlinear Equalizer With Improved Dynamic Impairment Tracking

We propose an unsupervised domain adaptation (UDA) based adaptive equalizer to compensate nonlinearity and dynamic timing jitter in intensity-modulation and direct-detection (IM/DD) systems. The proposed method employs generative adversarial training to update the equalizer on a block-by-block basis without the need for decision feedback. 56-Gbit/s PAM-4 experiments over 30-km fiber transmission under residual timing jitter, either virtually added using the mathematical model or resulted from Gardner-algorithm based digital phase-locked loop, show that the proposed UDA method has a faster convergence speed and better tracking performance than decision-directed neural network (NN) and virtual adversarial training (VAT) based NN, while exhibiting a lower complexity than VAT.