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.
| Original language | English |
|---|---|
| Pages (from-to) | 4751-4755 |
| Number of pages | 5 |
| Journal | Journal of Lightwave Technology |
| Volume | 42 |
| Issue number | 14 |
| Early online date | 25 Mar 2024 |
| DOIs | |
| Publication status | Published - 15 Jul 2024 |
Bibliographical note
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.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