  In this work, we address the paramount question of generalizability and
adaptability of artificial neural networks (NNs) used for impairment mitigation
in optical transmission systems. We demonstrate that by using well-developed
techniques based on the concept of transfer learning, we can efficaciously
retrain NN-based equalizers to adapt to changes in the transmission system
using just a fraction of the initial training data and resources. We evaluate
the potential of transfer learning to adapt the NN to changes in the launch
powers, modulation formats, symbol rates, or even fiber plant (different fiber
types and lengths). In our numerical examples, we consider the recently
introduced combined NN equalizer consisting of a convolutional layer coupled
with bi-directional long-short term memory (biLSTM) recurrent NN elements. We
focus our analysis on long-haul coherent optical transmission systems employing
two types of transmission fibers: the standard single-mode fiber (SSMF) and the
TrueWave Classic (TWC) fiber. We also underline the specific peculiarities that
occur when transferring the learning in coherent optical communication systems.
Our results demonstrate the effectiveness of transfer learning for the fast
adaptation of NN architectures to different transmission regimes and scenarios,
paving the way for engineering flexible universal solutions for nonlinearity
mitigation.
