  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.
