Query-Age-Optimal Scheduling under Sampling and Transmission Constraints
This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI)...
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creator | Zakeri, Abolfazl Moltafet, Mohammad Leinonen, Markus Codreanu, Marian |
description | This letter provides query-age-optimal joint sampling and transmission
scheduling policies for a heterogeneous status update system, consisting of a
stochastic arrival and a generate-at-will source, with an unreliable channel.
Our main goal is to minimize the average query age of information (QAoI)
subject to average sampling, average transmission, and per-slot transmission
constraints. To this end, an optimization problem is formulated and solved by
casting it into a linear program. We also provide a low-complexity near-optimal
policy using the notion of weakly-coupled constrained Markov decision
processes. The numerical results show up to 32% performance improvement by the
proposed policies compared with a benchmark policy. |
doi_str_mv | 10.48550/arxiv.2209.11689 |
format | Article |
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scheduling policies for a heterogeneous status update system, consisting of a
stochastic arrival and a generate-at-will source, with an unreliable channel.
Our main goal is to minimize the average query age of information (QAoI)
subject to average sampling, average transmission, and per-slot transmission
constraints. To this end, an optimization problem is formulated and solved by
casting it into a linear program. We also provide a low-complexity near-optimal
policy using the notion of weakly-coupled constrained Markov decision
processes. The numerical results show up to 32% performance improvement by the
proposed policies compared with a benchmark policy.</description><identifier>DOI: 10.48550/arxiv.2209.11689</identifier><language>eng</language><creationdate>2022-09</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2209.11689$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2209.11689$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Zakeri, Abolfazl</creatorcontrib><creatorcontrib>Moltafet, Mohammad</creatorcontrib><creatorcontrib>Leinonen, Markus</creatorcontrib><creatorcontrib>Codreanu, Marian</creatorcontrib><title>Query-Age-Optimal Scheduling under Sampling and Transmission Constraints</title><description>This letter provides query-age-optimal joint sampling and transmission
scheduling policies for a heterogeneous status update system, consisting of a
stochastic arrival and a generate-at-will source, with an unreliable channel.
Our main goal is to minimize the average query age of information (QAoI)
subject to average sampling, average transmission, and per-slot transmission
constraints. To this end, an optimization problem is formulated and solved by
casting it into a linear program. We also provide a low-complexity near-optimal
policy using the notion of weakly-coupled constrained Markov decision
processes. The numerical results show up to 32% performance improvement by the
proposed policies compared with a benchmark policy.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7FOwzAYBGAvDKjwAEz4BRzsOHbtsYqAIlWqULNHf_LbxVLiRnaC6NsDgel0y-k-Qh4ELyqjFH-C9BU-i7LkthBCG3tL9u-LS1e2Ozt2nOYwwkBP_YfDZQjxTJeILtETjNNaISJtEsQ8hpzDJdL6EvOcIMQ535EbD0N29_-5Ic3Lc1Pv2eH4-lbvDgz01jLF0TiH2vgOPUhf8c5z7AHKSkppdGm4EwjYKwEShJZlpXorbMeN2xrkckMe_2ZXSjuln8vp2v6S2pUkvwEaakek</recordid><startdate>20220923</startdate><enddate>20220923</enddate><creator>Zakeri, Abolfazl</creator><creator>Moltafet, Mohammad</creator><creator>Leinonen, Markus</creator><creator>Codreanu, Marian</creator><scope>GOX</scope></search><sort><creationdate>20220923</creationdate><title>Query-Age-Optimal Scheduling under Sampling and Transmission Constraints</title><author>Zakeri, Abolfazl ; Moltafet, Mohammad ; Leinonen, Markus ; Codreanu, Marian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-50d8eed68fbdfa3f40bf0dcaa2433386280e1dadc51a3a163245c919b08e78d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Zakeri, Abolfazl</creatorcontrib><creatorcontrib>Moltafet, Mohammad</creatorcontrib><creatorcontrib>Leinonen, Markus</creatorcontrib><creatorcontrib>Codreanu, Marian</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zakeri, Abolfazl</au><au>Moltafet, Mohammad</au><au>Leinonen, Markus</au><au>Codreanu, Marian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Query-Age-Optimal Scheduling under Sampling and Transmission Constraints</atitle><date>2022-09-23</date><risdate>2022</risdate><abstract>This letter provides query-age-optimal joint sampling and transmission
scheduling policies for a heterogeneous status update system, consisting of a
stochastic arrival and a generate-at-will source, with an unreliable channel.
Our main goal is to minimize the average query age of information (QAoI)
subject to average sampling, average transmission, and per-slot transmission
constraints. To this end, an optimization problem is formulated and solved by
casting it into a linear program. We also provide a low-complexity near-optimal
policy using the notion of weakly-coupled constrained Markov decision
processes. The numerical results show up to 32% performance improvement by the
proposed policies compared with a benchmark policy.</abstract><doi>10.48550/arxiv.2209.11689</doi><oa>free_for_read</oa></addata></record> |
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title | Query-Age-Optimal Scheduling under Sampling and Transmission Constraints |
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