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Modulation Scheme Identification Based on Artificial Neural Network Algorithms for Optical Communication System

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 1 (2020) · pp. 69–77

DOI
10.5614/itbj.ict.res.appl.2020.14.1.5
ISSN
2338-5499, 2337-5787
Published
2020-07-28
Publisher
DRI - ITB
Language
eng

Abstract

Higher-order modulation schemes in optical communication systems that suffer from several impairments can use artificial intelligence (AI) algorithms, among other possible techniques, to mitigate these issues. In this paper, several techniques for optical communication systems have been proposed to enhance the performance of dual-polarization (DP) M-ary Quadrature Amplitude Modulation (M-QAM) as DP-16-QAM, DP-64-QAM, DP-128-QAM, and DP-256-QAM with 240Gbps data rate. Artificial neural networks (ANNs) with seven different training algorithms have been applied to optimize the optical communication system. A high optimization of modulation format identification (MFI) with accuracy up to 100% was obtained at about 13 dB OSNR and at 22 dB OSNR for the DP-265-QAM format.

Keywords

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3 authors · 3 co-authorship links

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  • Al-Esraa University
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Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Modulation Scheme Identification Based on Artificial Neural Network Algorithms for Optical Communication System</dc:title>
	<dc:creator>Jalil, Mustafa A.</dc:creator>
	<dc:creator>Ayad, Jenan</dc:creator>
	<dc:creator>Abdulkareem, Hanan J.</dc:creator>
	<dc:subject xml:lang="en-US">artificial neural network (ANN)</dc:subject>
	<dc:subject xml:lang="en-US">back-propagation</dc:subject>
	<dc:subject xml:lang="en-US">optical system optimization</dc:subject>
	<dc:subject xml:lang="en-US">modulation format identification (MFI)</dc:subject>
	<dc:description xml:lang="en-US">Higher-order modulation schemes in optical communication systems that suffer from several impairments can use artificial intelligence (AI) algorithms, among other possible techniques, to mitigate these issues. In this paper, several techniques for optical communication systems have been proposed to enhance the performance of dual-polarization (DP) M-ary Quadrature Amplitude Modulation (M-QAM) as DP-16-QAM, DP-64-QAM, DP-128-QAM, and DP-256-QAM with 240Gbps data rate. Artificial neural networks (ANNs) with seven different training algorithms have been applied to optimize the optical communication system. A high optimization of modulation format identification (MFI) with accuracy up to 100% was obtained at about 13 dB OSNR and at 22 dB OSNR for the DP-265-QAM format.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2020-07-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/13108</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2020.14.1.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 1 (2020); 69-77</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2020.14.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/13108/4860</dc:relation>
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