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dc.contributor.authorBogoni, A.
dc.contributor.authorFern&#225, o
dc.contributor.authorndez, E.
dc.contributor.authorC&#225, a
dc.contributor.authorrdenas Soto, A.
dc.contributor.authorGuerrero Gonzalez, N.
dc.contributor.authorSerafino, G.
dc.contributor.authorGhelfi, P.
dc.date.issued2020
dc.date.submitted2021-06-02T10:13:21Z
dc.identifierONIX_20210602_10.5772/intechopen.88871_480
dc.identifierhttps://library.oapen.org/handle/20.500.12657/49366
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/70658
dc.description.abstractNonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationthema EDItEUR::U Computing and Information Technologyen_US
dc.subject.othernonlinear phase noise, clustering, Voronoi, decision boundary
dc.titleChapter Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems
dc.typechapter
oapen.identifier.doi10.5772/intechopen.88871
oapen.relation.isPublishedBy035ecc65-6737-43cf-a13a-6bdf67ce01f4
dc.relationisFundedByH2020-MSCA-ITN-2014


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