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Open AccessArticle

A Blind Nonlinearity Compensator Using DBSCAN Clustering for Coherent Optical Transmission Systems

1
Radio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin, Ireland
2
Computer Engineering Department, Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia
3
Insight Centre for Data Analytics, School of Electronic Engineering, Dublin City University, Dublin 9, Ireland
4
Vision Systems Group, School of Electronic Engineering, Dublin City University, Dublin 9, Ireland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4398; https://doi.org/10.3390/app9204398
Received: 29 August 2019 / Revised: 3 October 2019 / Accepted: 7 October 2019 / Published: 17 October 2019
(This article belongs to the Special Issue Optics for AI and AI for Optics)
Coherent fiber-optic communication systems are limited by the Kerr-induced nonlinearity. Benchmark optical and digital nonlinearity compensation techniques are typically complex and tackle deterministic-induced nonlinearities. However, these techniques ignore the impact of stochastic nonlinear distortions in the network, such as the interaction of fiber nonlinearity with amplified spontaneous emission from optical amplification. Unsupervised machine learning clustering (e.g., K-means) has recently been proposed as a practical approach to the blind compensation of stochastic and deterministic nonlinear distortions. In this work, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed, for the first time, for blind nonlinearity compensation. DBSCAN is tested experimentally in a 40 Gb/s 16 quadrature amplitude-modulated system at 50 km of standard single-mode fiber transmission. It is shown that at high launched optical powers, DBSCAN can offer up to 0.83 and 8.84 dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalisation, respectively. View Full-Text
Keywords: fiber optics communications; coherent communications; machine learning; clustering; nonlinearity cancellation fiber optics communications; coherent communications; machine learning; clustering; nonlinearity cancellation
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Giacoumidis, E.; Lin, Y.; Jarajreh, M.; O’Duill, S.; McGuinness, K.; Whelan, P.F.; Barry, L.P. A Blind Nonlinearity Compensator Using DBSCAN Clustering for Coherent Optical Transmission Systems. Appl. Sci. 2019, 9, 4398.

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