文章基本信息

标题：Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
作者：Elias Giacoumidis ; Elias Giacoumidis ; Yi Lin 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2019
卷号：11
期号：1
页码：2
DOI：10.3390/fi11010002
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：fiber optics communications; machine learning; artificial neural network; support vector machine; clustering; nonlinear equalization; coherent optical OFDM fiber optics communications ; machine learning ; artificial neural network ; support vector machine ; clustering ; nonlinear equalization ; coherent optical OFDM