A Data-driven Optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities
arXiv:2106.05088 · doi:10.1109/IPC53466.2022.9975602
Abstract
We study the performance of gradient-descent optimization to estimate the coefficients of the discrete-time first-order regular perturbation (FRP). With respect to numerically computed coefficients, the optimized coefficients yield a model that (i) extends the FRP range of validity, and (ii) reduces the model's complexity.