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

Elias Giacoumidis, Yi-lin, Jinlong Wei, Ivan Aldaya, Athanasios Tsokanos, Liam Barry

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)
    55 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)1-20
    Number of pages20
    JournalFuture Internet
    Volume11
    Issue number1
    DOIs
    Publication statusPublished - 20 Dec 2018

    Fingerprint

    Dive into the research topics of 'Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM'. Together they form a unique fingerprint.

    Cite this