Chapter Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems (Record no. 43776)

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control field https://directory.doabooks.org/handle/20.500.12854/70658
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number intechopen.88871
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.5772/intechopen.88871
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041 0# - LANGUAGE CODE
Language code of text/sound track or separate title English
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072 #7 - SUBJECT CATEGORY CODE
Subject category code U
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100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Bogoni, A.
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245 10 - TITLE STATEMENT
Title Chapter Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. InTechOpen
Date of publication, distribution, etc. 2020
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
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Standardized terminology for access restriction Unrestricted online access
520 ## - SUMMARY, ETC.
Summary, etc. Nonlinear 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.
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Terms governing use and reproduction Creative Commons
Use and reproduction rights https://creativecommons.org/licenses/by/3.0/
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-- https://creativecommons.org/licenses/by/3.0/
546 ## - LANGUAGE NOTE
Language note English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computing & information technology
Source of heading or term bicssc
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term nonlinear phase noise, clustering, Voronoi, decision boundary
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Fern&#225, o
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name ndez, E.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name C&#225, a
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name rdenas Soto, A.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Guerrero Gonzalez, N.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Serafino, G.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ghelfi, P.
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Host Biblionumber OAPEN Library ID: ONIX_20210602_10.5772/intechopen.88871_480
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Public note DOAB: download the publication
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Uniform Resource Identifier <a href="https://directory.doabooks.org/handle/20.500.12854/70658">https://directory.doabooks.org/handle/20.500.12854/70658</a>
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