Adaptive Optimization of Quantized Perturbation Coefficients for Fiber Nonlinearity Compensation

In the perturbation-based nonlinearity compensation (PB-NLC) technique, quantization of perturbation coefficients is employed for reduction of computational and implementation complexity. In this paper, we propose and experimentally verify the adoption of a decision-directed least mean square algorithm for optimization and complexity reduction of PB-NLC equalizer. We show that for 32-GBaud dual polarization 16-QAM after 2560 km of single-mode fiber, the proposed scheme further reduces the computational term by 46%, compared with that of conventional PB-NLC with uniform quantization of perturbation coefficients for the same Q-factor improvement.