Learning-augmented Online Minimization of Age of Information and Transmission Costs
We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness co...
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| Vydané v: | IEEE Conference on Computer Communications workshops (Online) s. 01 - 08 |
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| Hlavní autori: | , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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IEEE
20.05.2024
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| ISSN: | 2833-0587 |
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| Abstract | We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst-case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning-auamented algorithm achieves both consistency and robustness. |
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| AbstractList | We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst-case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning-auamented algorithm achieves both consistency and robustness. |
| Author | Ji, Bo Zhang, Keyuan Li, Bin Sun, Yin Liu, Zhongdong Hou, Y. Thomas |
| Author_xml | – sequence: 1 givenname: Zhongdong surname: Liu fullname: Liu, Zhongdong email: zhongdong@vt.edu organization: Virginia Tech,Department of Computer Science,Blacksburg,VA – sequence: 2 givenname: Keyuan surname: Zhang fullname: Zhang, Keyuan email: keyuanz@vt.edu organization: Virginia Tech,Department of Computer Science,Blacksburg,VA – sequence: 3 givenname: Bin surname: Li fullname: Li, Bin email: binli@psu.edu organization: Pennsylvania State University,Department of Electrical Engineering,University Park,PA – sequence: 4 givenname: Yin surname: Sun fullname: Sun, Yin email: yzs0078@auburn.edu organization: Auburn University,Department of Electrical and Computer Engineering,,Auburn,AL – sequence: 5 givenname: Y. Thomas surname: Hou fullname: Hou, Y. Thomas email: thou@vt.edu organization: Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA – sequence: 6 givenname: Bo surname: Ji fullname: Ji, Bo email: boji@vt.edu organization: Virginia Tech,Department of Computer Science,Blacksburg,VA |
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| Snippet | We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its time-sensitive data to a destination over a... |
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| SubjectTerms | Age-of-Information Approximation algorithms Costs learning-augmented algorithm Machine learning algorithms Minimization online algorithm Prediction algorithms transmission cost Wireless communication Wireless sensor networks |
| Title | Learning-augmented Online Minimization of Age of Information and Transmission Costs |
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