Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
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Data
2018-12-20
Autores
Giacoumidis, Elias
Lin, Yi
Wei, Jinlong
Aldaya, Ivan [UNESP]
Tsokanos, Athanasios
Barry, Liam P.
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Resumo
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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Artificial neural network, Clustering, Coherent optical OFDM, Fiber optics communications, Machine learning, Nonlinear equalization, Support vector machine
Como citar
Future Internet, v. 11, n. 1, 2018.