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Optimizing Information Freshness in Mobile Networks with Age-Threshold ALOHA
Zhejiang Univ, Peoples R China.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4416-7702
Chongqing Univ, Peoples R China.
Sun Yat Sen Univ, Peoples R China.
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2024 (English)In: 2024 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, ISIT 2024, IEEE , 2024, p. 963-968Conference paper, Published paper (Refereed)
Abstract [en]

We optimize the Age of Information (AoI) in random access networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. We derive analytical expressions for the transmission success probability and time-average AoI using stochastic geometry tools. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the timeaverage AoI. In addition, we establish a scaling law for the time-average AoI in random access networks, revealing that the optimal time-average AoI increases linearly with the deployment density. Notably, the growth rate under TSA is half of that under conventional slotted ALOHA.

Place, publisher, year, edition, pages
IEEE , 2024. p. 963-968
Series
IEEE International Symposium on Information Theory, ISSN 2157-8095, E-ISSN 2157-8117
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:liu:diva-212448DOI: 10.1109/ISIT57864.2024.10619121ISI: 001304426901009Scopus ID: 2-s2.0-85202843988ISBN: 9798350382853 (print)ISBN: 9798350382846 (electronic)OAI: oai:DiVA.org:liu-212448DiVA, id: diva2:1945999
Conference
IEEE International Symposium on Information Theory (ISIT), Athens, GREECE, jul 07-12, 2024
Note

Funding Agencies|National Natural Science Foundation of China [62201504]; Zhejiang Lab Open Research Project [K2022PD0AB05]; Zhejiang-Singapore Innovation and AI Joint Research Lab

Available from: 2025-03-20 Created: 2025-03-20 Last updated: 2025-03-20

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
More languages
Output format
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