Inicio  /  Future Internet  /  Vol: 11 Par: 1 (2019)  /  Artículo
ARTÍCULO
TITULO

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

Elias Giacoumidis    
Yi Lin    
Jinlong Wei    
Ivan Aldaya    
Athanasios Tsokanos and Liam P. Barry    

Resumen

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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