Journal
FUTURE INTERNET
Volume 11, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/fi11010002
Keywords
fiber optics communications; machine learning; artificial neural network; support vector machine; clustering; nonlinear equalization; coherent optical OFDM
Categories
Funding
- EU Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie [713567]
- Science Foundation Ireland
- European Regional Development Fund [13/RC/2077]
- Marie Curie Actions (MSCA) [713567] Funding Source: Marie Curie Actions (MSCA)
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Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.
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