Application of complex fully connected neural networks to compensate for nonlinearity in fibre-optic communication lines with polarisation division multiplexing

Abstract

A scheme is proposed to compensate for nonlinear distortions in extended fibre-optic communication lines with polarisation division multiplexing, based on fully connected neural networks with complex-valued arithmetic. The activation function of the developed scheme makes it possible to take into account the nonlinear interaction of signals from different polarisation components. This scheme is compared with a linear one and a neural network that processes signals of different polarisations independently, and the superiority of the proposed neural network architecture is demonstrated.