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Accessibility Degradation Prediction on LTE/SAE Network Using Discrete Time Markov Chain (DTMC) Model

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 1 (2019) · pp. 1–18

DOI
10.5614/itbj.ict.res.appl.2019.13.1.1
ISSN
2338-5499, 2337-5787
Published
2019-04-30
Publisher
DRI - ITB
Language
eng

Abstract

In this paper, an algorithm for predicting accessibility performance on an LTE/SAE network based on relevant historical key performance indicator (KPI) data is proposed. Since there are three KPIs related to accessibility, each representing different segments, a method to map these three KPI values onto the status of accessibility performance is proposed. The network conditions are categorized as high, acceptable or low for each time interval of observation. The first state shows that the system is running optimally, while the second state shows that the system has deteriorated and needs full attention, and the third state indicates that the system has gone into degraded conditions that cannot be tolerated. After the state sequence has been obtained, a transition probability matrix can be derived, which can be used to predict future conditions using a DTMC model. The results obtained are system predictions in terms of probability values for each state for a specific future time. These prediction values are required for proactive health monitoring and fault management. Accessibility degradation prediction is then conducted by using measurement data derived from an eNodeB in the LTE network for a period of one month.

Keywords

Collaboration network of the authors

7 authors · 12 co-authorship links

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  • Bandung Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ismail IsmailBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Basuki Y. IskandarAffiliation unknown1
Iskandar IskandarBandung Institute of Technology1
Veronica Windha MahyastutyBandung Institute of Technology1
Mohammad Sigit ArifiantoBandung Institute of Technology1

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">Accessibility Degradation Prediction on LTE/SAE Network Using Discrete Time Markov Chain (DTMC) Model</dc:title>
	<dc:creator>Hendrawan, Hendrawan</dc:creator>
	<dc:subject xml:lang="en-US">LTE/SAE Network</dc:subject>
	<dc:subject xml:lang="en-US">DTMC</dc:subject>
	<dc:subject xml:lang="en-US">KPI</dc:subject>
	<dc:subject xml:lang="en-US">SON</dc:subject>
	<dc:subject xml:lang="en-US">Self-Healing</dc:subject>
	<dc:subject xml:lang="en-US">accessibility</dc:subject>
	<dc:subject xml:lang="en-US">degradation prediction</dc:subject>
	<dc:description xml:lang="en-US">In this paper, an algorithm for predicting accessibility performance on an LTE/SAE network based on relevant historical key performance indicator (KPI) data is proposed. Since there are three KPIs related to accessibility, each representing different segments, a method to map these three KPI values onto the status of accessibility performance is proposed. The network conditions are categorized as high, acceptable or low for each time interval of observation. The first state shows that the system is running optimally, while the second state shows that the system has deteriorated and needs full attention, and the third state indicates that the system has gone into degraded conditions that cannot be tolerated. After the state sequence has been obtained, a transition probability matrix can be derived, which can be used to predict future conditions using a DTMC model. The results obtained are system predictions in terms of probability values for each state for a specific future time. These prediction values are required for proactive health monitoring and fault management. Accessibility degradation prediction is then conducted by using measurement data derived from an eNodeB in the LTE network for a period of one month.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-04-30</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/7410</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.1.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 1 (2019); 1-18</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2019.13.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/7410/4172</dc:relation>
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