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- W4313854360 abstract "The ever-increasing needs of supporting real-time applications have spurred new studies on minimizing Age-of-Information (AoI), a novel metric characterizing the data freshness of the system. This work studies the single-queue information update system and strengthens the seminal results of Sun et al. on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>fixed-point-based</i> method is proposed with <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>quadratic convergence rate</i> , an order-of-magnitude improvement over the state-of-the-art; (ii) When the distributional knowledge is unavailable (which is the norm in practice), two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>upgrade</i> certain components to handle additional practical challenges. Two such upgrades are proposed for the situations: (iii.1) The AoI penalty function is also unknown and must be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. The performance of our schemes is either provably optimal or within 3% of the omniscient optimal offline solutions in all simulation scenarios." @default.
- W4313854360 created "2023-01-10" @default.
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- W4313854360 date "2023-08-01" @default.
- W4313854360 modified "2023-10-16" @default.
- W4313854360 title "Distribution-Oblivious Online Algorithms for Age-of-Information Penalty Minimization" @default.
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- W4313854360 doi "https://doi.org/10.1109/tnet.2022.3230009" @default.
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