Computing Continuous Nonlinear Fourier Spectrum of Optical Signal with Artificial Neural Networks
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Nonlinear Fourier transform (NFT) (also known in the mathematical and nonlinear science community as the inverse scattering transform) has recently attracted a great deal of attention in the context of optical transmission in fiber channels, that can be approximated by the nonlinear Schrodinger equation. Within the NFT-based transmission approach, we modulate the parameters of the nonlinear spectrum (NS) and generate the respective information signal in time domain using inverse NFT. Both discrete and continuous parts of NS can be used, here we focus on the continuous spectrum only. Then, the signal is launched into the fiber, and at the receiver we apply direct NFT to the received signal’s to retrieve the information encoded in NS. In this work we demonstrate that the high-accuracy computation of the continuous NS can be performed by using artificial neural networks (NN).
See conference thesis here