Open AccessNonlinear Fourier transform receiver based on a time domain diffractive deep neural network
Author(s) -
Junhe Zhou,
Qingsong Hu,
Haoqian Pu
Publication year - 2022
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.473373
Subject(s) - computer science , fourier transform , time domain , optics , artificial neural network , signal (programming language) , distortion (music) , decoding methods , signal processing , algorithm , telecommunications , artificial intelligence , physics , computer vision , bandwidth (computing) , amplifier , radar , quantum mechanics , programming language
A diffractive deep neural network (D2NN) is proposed to distinguish the inverse nonlinear Fourier transform (INFT) symbols. Different from other recently proposed D2NNs, the D2NN is fiber based, and it is in the time domain rather than the spatial domain. The D2NN is composed of multiple cascaded dispersive elements and phase modulators. An all-optical back-propagation algorithm is proposed to optimize the phase. The fiber-based time domain D2NN acts as a powerful tool for signal conversion and recognition, and it is used in a receiver to recognize the INFT symbols all optically. After the symbol conversion by the D2NN, simple phase and amplitude measurement will determine the correct symbol while avoiding the time-consuming NFT. The proposed device can not only be implemented in the NFT transmission system, but also in other areas which require all optical time domain signal transformation and recognition, like sensing, signal coding and decoding, beam distortion compensation and image recognition.