Combining Optical and Digital Compensation: Neural Network-Based Channel Equalisers in Dispersion-Managed Communications Systems

Abstract

Machine learning methods, including artificial neural networks, used for mitigation of nonlinear transmission impairments in ultra-long-haul and long-haul unmanaged fibre-optic links feature high complexity due to the accumulated dispersion resulting into large channel memory. Combination of the all-optical dispersion management techniques reducing effective channel memory and low-complexity digital post-processing potentially can offer an attractive trade-off between performance, complexity and costs (or power consumption). This paper demonstrates a feasibility of substantial complexity reduction of machine learning-based channel equalisation in dispersion-managed transmission with acceptable system performance.

Publication DOI: https://doi.org/10.1109/jlt.2024.3380998
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Aston University (General)
Funding Information: The authors would like to acknowledge the support of the EPSRC project TRANSNET, as well as EPSRC Core Equipment Fund, Grant EP/V036106/1, for providing the Aston EPS Machine Learning Server.
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: Artificial neural networks,Complexity theory,Dispersion,Equalizers,Erbium-doped fiber amplifiers,Fiber nonlinear optics,Neural networks,Optical distortion,dispersion management,nonlinear equaliser,optical communications,Atomic and Molecular Physics, and Optics
Publication ISSN: 0733-8724
Last Modified: 25 Nov 2024 08:52
Date Deposited: 17 May 2024 14:21
Full Text Link:
Related URLs: https://ieeexpl ... cument/10478538 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-07-15
Published Online Date: 2024-03-25
Accepted Date: 2024-03-14
Authors: Nurlybayeva, Karina
Kamalian-Kopae, Morteza
Turitsyna, Elena
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 25 March 2025.


Export / Share Citation


Statistics

Additional statistics for this record