A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

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.

Improving Optical Transmission Performance in Multi- Channel Networks Utilizing CNN-Enabled Digital Signal Processing to Mitigate Four-Wave Mixing

“…In this paper, we have investigated a machine learning-based convolutional neural network model for optical communication networks to achieve high capacity, long distance, and multi-channel transmission. We have compared our results with conventional approaches as discussed in [9] to [17], and found that our model has superior performance in terms of bit error ratio, optical signal-to-noise ratio, spectral e ciency, and power e ciency. We have also studied the four-wave mixing nonlinear cause, which severely degrades the quality of the transmitted signal at the receiver side.…”

“…Previously, many methods were used for compensation the effects of FWM nonlinear cause and enhance the SE of large distance and multi-channel OCN. In reference [9], the newest ML techniques were analyzed for modern elastic OCN to improve multi-channel transmissions. The optical orthogonal frequency domain multiplexing (O-OFDM) is measured for OAN.…”