A neural network for forward and inverse nonlinear Fourier transforms for fiber optic communication
Signal Processing
2024-07-17 v1 Optics
Abstract
We propose a neural network for both forward and inverse continuous nonlinear Fourier transforms, NFT and INFT respectively. We demonstrate the network's capability to perform NFT and INFT for a random mix of NFDM-QAM signals. The network transformations (NFT and INFT) exhibit true characteristics of these transformations; they are significantly different for low and high-power input pulses. The network shows adequate accuracy with an RMSE of 5e-3 for forward and 3e-2 for inverse transforms. We further show that the trained network can be used to perform general nonlinear Fourier transforms on arbitrary pulses beyond the training pulse types.
Cite
@article{arxiv.2407.11093,
title = {A neural network for forward and inverse nonlinear Fourier transforms for fiber optic communication},
author = {Wen Qi Zhang and Terence H. Chan and Shahraam Afshar V.},
journal= {arXiv preprint arXiv:2407.11093},
year = {2024}
}