Self-supervised learning for neural-network-based perturbative fiber nonlinearity compensation

A self-supervised learning scheme is proposed for neural-network-based perturbative fiber nonlinearity compensation with a designed proxy task by using phase-conjugated unknown sequences. The self-supervised trained model can be applied directly to the corresponding dual-polarization system with no obvious performance penalty compared with the supervised one.