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Jurnal SINTA 2

Big Data Assisted CRAN Enabled 5G SON Architecture

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 2 (2019) · pp. 93–106

DOI
10.5614/itbj.ict.res.appl.2019.13.2.1
ISSN
2338-5499, 2337-5787
Published
2019-09-30
Publisher
DRI - ITB
Language
eng

Abstract

The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem.

Keywords

Collaboration network of the authors

5 authors · 10 co-authorship links · 4 across different organisations

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  • National University of Sciences and Technology
  • University of Bologna
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

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">Big Data Assisted CRAN Enabled 5G SON Architecture</dc:title>
	<dc:creator>Khurshid, Kiran</dc:creator>
	<dc:creator>Khan, Adnan Ahmed</dc:creator>
	<dc:creator>Siddiqui, Haroon</dc:creator>
	<dc:creator>Rashid, Imran</dc:creator>
	<dc:creator>Hadi, M. Usman</dc:creator>
	<dc:subject xml:lang="en-US">5G</dc:subject>
	<dc:subject xml:lang="en-US">big data</dc:subject>
	<dc:subject xml:lang="en-US">CRAN</dc:subject>
	<dc:subject xml:lang="en-US">NVF</dc:subject>
	<dc:subject xml:lang="en-US">SDN</dc:subject>
	<dc:subject xml:lang="en-US">self-optimization.</dc:subject>
	<dc:description xml:lang="en-US">The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-09-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<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/9076</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.2.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 2 (2019); 93-106</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/9076/4339</dc:relation>
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