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A blind nonlinearity compensator using DBSCAN clustering for coherent optical transmission systems

Giacoumidis, Elias orcid logoORCID: 0000-0002-1161-7837, Lin, Yi, Jarajreh, Mutsam, O'Duill, Sean orcid logoORCID: 0000-0002-7690-4474, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and Whelan, Paul F. orcid logoORCID: 0000-0001-9230-7656 (2019) A blind nonlinearity compensator using DBSCAN clustering for coherent optical transmission systems. Applied Sciences, 9 (20). ISSN 2076-3417

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.
Item Type:Article (Published)
Uncontrolled Keywords:fiber optics communications; coherent communications; machine learning; clustering; nonlinearity cancellation
Subjects:Engineering > Electronics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://dx.doi.org/10.3390/app9204398
Copyright Information:© 2019 The Authors. Open Access (CC-BY 4.0)
ID Code:27551
Deposited On:12 Aug 2022 17:14 by Thomas Murtagh . Last Modified 10 Jan 2023 14:02

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