Machine Learning Methods for Compensating Signal Distortions in Fiber-Optic Communication Lines

The article addresses current issues in the field of fiber-optic data transmission, related to the constant increase in demand for communication system bandwidth and nonlinear response. The main machine learning methods used to compensate for nonlinear signal distortions in long-haul coherent commun...

Full description

Saved in:
Bibliographic Details
Published in:Optoelectronics, instrumentation, and data processing instrumentation, and data processing, 2024-02, Vol.60 (1), p.1-10
Main Authors: Sidelnikov, O. S., Redyuk, A. A., Fedoruk, M. P.
Format: Article
Language:English
Subjects:
Artificial Intelligence Methods
Lasers
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The article addresses current issues in the field of fiber-optic data transmission, related to the constant increase in demand for communication system bandwidth and nonlinear response. The main machine learning methods used to compensate for nonlinear signal distortions in long-haul coherent communication lines are presented, including neural networks of various architectures. The paper emphasizes the promise of machine learning-based solutions for enhancing the performance of optical fiber communication systems, thanks to their ability to derive effective and adaptive signal recovery schemes with low computational complexity.
ISSN:8756-6990
1934-7944
DOI:10.3103/S8756699024700018