Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Autor:	Elias Giacoumidis, Yi Lin, Jinlong Wei, Ivan Aldaya, Athanasios Tsokanos, Liam P. Barry
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	fiber optics communications machine learning artificial neural network support vector machine clustering nonlinear equalization coherent optical OFDM Information technology T58.5-58.64
Zdroj:	Future Internet, Vol 11, Iss 1, p 2 (2018)
Druh dokumentu:	article
ISSN:	1999-5903 11010002
DOI:	10.3390/fi11010002
Popis:	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.
Databáze:	Directory of Open Access Journals
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