Finite-Genus Solutions-based Optical Communication with the Riemann-Hilbert Problem Transmitter and a Convolutional Neural Network Receiver

Abstract

In the study, we develop a new optical communication system based on the nonlinear Fourier transform for generic (quasiperiodic) finite-genus solutions to the nonlinear Schrödinger equation. At the transmitter, the finite-genus solutions of a generic type, which are not subject to any periodicity constraint, are generated by means of the Riemann-Hilbert problem (RHP) approach and utilized as the data carriers. Data encoding is achieved by modulating the phases of these solutions (as defined by the RHP), whereas the main spectrum determines signal parameters such as duration, bandwidth, and power. To decode the phases and compensate for their evolution, we propose a receiver that employs a convolutional neural network (CNN). CNN helps us overcome the lack of a complete theoretical framework for generic finite-genus solutions. We carry out the numerical simulations of a communication system that utilizes the phases of finite-genus solutions as data carriers with a CNN-based receiver. We present an analysis of the system's performance in terms of bit error ratio (BER) in dependence on signal power, propagation distance, and sampling rate. Additionally, we investigate the ability of the CNN-based receiver to process signals with a truncated linear spectrum to provide higher spectral efficiency, attaining 4.28bits/s/Hz (for a single polarization) at 1040km transmission distance.

Publication DOI: https://doi.org/10.1109/JLT.2024.3398561
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Aston University (General)
Funding Information: Stepan Bogdanov and Jaroslaw E. Prilepsky acknowledge Leverhulme Trust project, Grant No. RPG-2018-063.
Additional Information: Copyright © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Bandwidth,Convolutional neural network,Convolutional neural networks,Inverse problems,Optical fibers,Optical receivers,Optical transmitters,Phase modulation,Riemann-Hilbert problem,fibre-optic communications,finite-genus solutions,nonlinear Fourier transform,Atomic and Molecular Physics, and Optics
Publication ISSN: 0733-8724
Last Modified: 25 Nov 2024 08:53
Date Deposited: 29 May 2024 14:27
Full Text Link:
Related URLs: https://ieeexpl ... cument/10522824 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-08-15
Published Online Date: 2024-05-08
Accepted Date: 2024-05-06
Authors: Bogdanov, Stepan
Shepelsky, Dmitry
Kamalian Kopae, Morteza (ORCID Profile 0000-0002-6278-976X)
Vasylchenkova, Anastasiia
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)

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