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 |
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Pages (from-to) | 1-6 |
Number of pages | 6 |
Journal | Journal of Lightwave Technology |
DOIs | |
Publication status | E-pub ahead of print - 24 Mar 2024 |
Bibliographical note
Publisher Copyright:IEEE
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