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A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks

Hichem Mrabet Elias Giaccoumidis Iyad Dayoub 1 
1 COMNUM - IEMN - COMmunications NUMériques - IEMN
INSA Hauts-De-France - INSA Institut National des Sciences Appliquées Hauts-de-France, IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
Abstract : In this survey, we analyze the newest machine learning (ML) techniques for optical orthogonal frequency division multiplexing (O-OFDM)-based optical communications. ML has been proposed to mitigate channel and transceiver imperfections. For instance, ML can improve the signal quality under low modulation extinction ratio or can tackle both determinist and stochastic-induced nonlinearities such as parametric noise amplification in long-haul transmission. The proposed ML algorithms for O-OFDM can in particularly tackle inter-subcarrier nonlinear effects such as four-wave mixing and cross-phase modulation. In essence, these ML techniques could be beneficial for any multi-carrier approach (e.g. filter bank modulation). Supervised and unsupervised ML techniques are analyzed in terms of both O-OFDM transmission performance and computational complexity for potential real-time implementation. We indicate the strict conditions under which a ML algorithm should perform classification, regression or clustering. The survey also discusses open research issues and future directions towards the ML implementation.
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Preprints, Working Papers, ...
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Contributor : Iyad Dayoub Connect in order to contact the contributor
Submitted on : Monday, October 18, 2021 - 2:51:24 PM
Last modification on : Tuesday, July 19, 2022 - 11:46:59 AM

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  • HAL Id : hal-03383143, version 1
  • ARXIV : 2105.03289


Hichem Mrabet, Elias Giaccoumidis, Iyad Dayoub. A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks. 2021. ⟨hal-03383143⟩



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