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

A scheme is proposed to compensate for nonlinear distor-tions 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 nonlin-ear 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 dem-onstrated.

Automated summary

A scheme utilizing fully connected neural networks with complex-valued arithmetic is proposed for compensating nonlinear distortions in extended fibre-optic communication lines with polarisation division multiplexing. The activation function of the developed scheme accounts for the nonlinear interaction of signals from different polarisation components, demonstrating the superiority of the proposed neural network architecture.