Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Artificial neural networks for nonlinear pulse shaping in optical fibers

We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated

Developing coherent optical wavelength conversion systems for reconfigurable photonic networks

In future optical networks that employ wavelength division multiplexing (WDM), the use of optical switching technologies on a burst or packet level, combined with advanced modulation formats would achieve greater spectral efficiency and utilize the existing bandwidth more efficiently. All-optical wavelength converters are expected to be one of the key components in these broadband networks. They can be used at the network nodes to avoid contention and to dynamically allocate wavelengths to ensure optimum use of fiber bandwidth. In this work, a reconfigurable wavelength converter comprising of a Semiconductor Optical Amplifier (SOA) as the nonlinear element and a fast-switching sampled grating distributed Bragg reflector (SG-DBR) tunable laser as one of the pumps is developed. The wavelength conversion of 12.5-Gbaud quadrature phase shift keying (QPSK) and Pol-Mul QPSK signals with switching time of tens of nanoseconds is experimentally achieved. Although the tunable DBR lasers can achieve ns tuning time, they present relatively large phase noise. The phase noise transfer from the pump to the converted signal can have a deleterious effect on signal quality and cause a performance penalty with phase modulated signals. To overcome the phase noise transfer issue, a wavelength converter using tunable dual-correlated pumps provided by the combination of a single-section quantum dash passively mode-locked laser (QD-PMLL) and a programmable tunable optical filter is designed and the wavelength conversion of QPSK and 16-quadrature amplitude modulation (16-QAM) signals at 12.5 GBaud is experimentally investigated. Nonlinear distortion of the wavelength converted signal caused by gain saturation effects in the SOA can significantly degrade the signal quality and cause difficulties for the practical wavelength conversion of sig nal data with advanced modulation formats. In this work, the machine learning clustering based nonlinearity compensation method is proposed to improve the tolerance to nonlinear distortion in an SOA based wavelength conversion system with 16 QAM and 64 QAM signals