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arXiv · 2106.05088

A Data-driven Optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities

Astrid Barreiro·Gabriele Liga·Alex Alvarado
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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.

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Astrid Barreiro, Gabriele Liga, Alex Alvarado. 2023-01-09. A Data-driven Optimization of First-order Regular Perturbation Coefficients for Fiber Nonlinearities. https://doi.org/10.1109/ipc53466.2022.9975602

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