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

TL;DR

This survey reviews recent machine learning methods applied to optical OFDM networks, highlighting their ability to mitigate nonlinear effects and improve signal quality in optical communications.

Contribution

It provides a comprehensive analysis of ML techniques for O-OFDM, including their performance, complexity, and potential for real-time implementation, along with future research directions.

Findings

ML can mitigate nonlinear effects like four-wave mixing.

Supervised and unsupervised ML techniques improve transmission performance.

Analysis of computational complexity for real-time applications.

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.