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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Bibliographic Details
Published in:IEEE Conference on Computer Communications workshops (Online) pp. 01 - 08
Main Authors: Liu, Zhongdong, Zhang, Keyuan, Li, Bin, Sun, Yin, Hou, Y. Thomas, Ji, Bo
Format: Conference Proceeding
Language:English
Published: IEEE 20.05.2024
Subjects:
Age-of-Information
Approximation algorithms
Costs
learning-augmented algorithm
Machine learning algorithms
Minimization
online algorithm
Prediction algorithms
transmission cost
Wireless communication
Wireless sensor networks
ISSN:2833-0587
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Summary: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.
ISSN:2833-0587
DOI:10.1109/INFOCOMWKSHPS61880.2024.10620883