  The demand for high speed data transmission has increased rapidly, leading to
advanced optical communication techniques. In the past few years, multiple
equalizers based on neural network (NN) have been proposed to recover signal
from nonlinear distortions. However, previous experiments mainly focused on
achieving low bit error rate (BER) on certain dataset with an offline-trained
NN, neglecting the generalization ability of NN-based equalizer when the
properties of optical link change. The development of efficient online training
scheme is urgently needed. In this paper, we've proposed an adaptive online
training scheme, which can fine-tune parameters of NN-based equalizer without
the help of an online training sequence. By introducing data augmentation and
virtual adversarial training, the convergence speed has been accelerated by 4.5
times, compared with decision-directed self-training. The proposed adaptive
NN-based equalizer is called "AdaNN". Its BER has been evaluated under two
scenarios: a 56 Gb/s PAM4-modulated VCSEL-MMF optical link (100-m), and a 32
Gbaud 16QAM-modulated Nyquist-WDM system (960-km SSMF). In our experiments,
with the help of AdaNN, BER values can be quickly stabilized below 1e-3 after
trained with 10^5 unlabeled symbols. AdaNN shows great performance improvement
compared with non-adaptive NN and conventional MLSE.
