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.
@article{arxiv.2106.05088,
title = {A Data-driven Optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities},
author = {Astrid Barreiro and Gabriele Liga and Alex Alvarado},
journal= {arXiv preprint arXiv:2106.05088},
year = {2023}
}