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-augmented algorithm achieves both consistency and robustness.

考虑到离散时间系统中的资源受限源，通过时变无线信道将其时间敏感的数据传输到目的地。我们开发了一种鲁棒的在线算法，以最小化传输和信息老化成本之和，确保最坏情况下的性能保证。