A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation

Abstract

We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is observed.Index Terms-digital back propagation, fiber nonlinearity mitigation, machine learning, optical communication systems, Parzen window.

Machine Learning for Fiber Nonlinearity Mitigation in Long-Haul Coherent Optical Transmission Systems : Invited Paper

“…The performance of a technique based on supervised algorithm, Parzen window (PW), which classifies symbols at Rx side based on the labeled training data generated before by associating a label to each symbol, is also presented. It provides slightly better than SVM, as a two-step nonlinearity mitigation method is employed by first applying DBP for equalizing deterministic nonlinear effects and then combating stochastic nonlinearities with the PW algorithm [20]. IV.…”

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

“…Many methodologies have been evaluated among which SVM [17], ANN [18] and Kernel based methods are most significant [19]. Recently, Parzen window method to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems has been proposed as well [20]. Finally, the other important application of ML method is smart decision in order to prevent any errors by fault detection.…”

Practically Feasible Closed-Loop Digital Predistortion for VCSEL-MMF-Based Radio-over-Fiber links

“…The problem is particularly present in RoF systems that utilize Vertical Cavity Surface Emitting Lasers (VCSELs). Indeed, these systems, which beside mobile fronthauling [10][11][12], find applications in ever-changing scenarios like indoor distribution of wireless signals [18], machine learning methods [19] and radio astronomy [20], are attractive because these optical sources feature low cost and low power consumption.…”