# Learning-augmented Online Minimization of Age of Information and Transmission Costs

arXiv: https://arxiv.org/abs/2403.02573 · HTML: https://mlsanity.com/paper/2403.02573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [The Primal-Dual method for Learning Augmented Algorithms](https://mlsanity.com/paper/2010.11632/) (2020)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Can Q-Learning be Improved with Advice?](https://mlsanity.com/paper/2110.13052/) (2021)
- [Smoothed Online Optimization with Unreliable Predictions](https://mlsanity.com/paper/2202.03519/) (2022)
- [Expert-Calibrated Learning for Online Optimization with Switching Costs](https://mlsanity.com/paper/2204.08572/) (2022)
- [Multi-UAV Interference Coordination via Joint Trajectory and Power Control](https://mlsanity.com/paper/1809.05697/) (2018)
- [Online Energy-Efficient Scheduling for Timely Information Downloads in Mobile Networks](https://mlsanity.com/paper/1901.03137/) (2019)
- [Learning-augmented Online Algorithm for Two-level Ski-rental Problem](https://mlsanity.com/paper/2402.06715/) (2024)
- [Optimal Sampling Cost in Wireless Networks with Age of Information Constraints](https://mlsanity.com/paper/2003.02512/) (2020)
- [Distortion Minimization with Age of Information and Cost Constraints](https://mlsanity.com/paper/2303.00850/) (2023)
- [Age of Information in the Presence of an Adversary](https://mlsanity.com/paper/2202.04050/) (2022)
- [Age of Information of a Power Constrained Scheduler in the Presence of a Power Constrained Adversary](https://mlsanity.com/paper/2301.01276/) (2023)

## Built on by (most cited first, 2 of 2)

- [Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics](https://mlsanity.com/paper/2509.18654/) (2025)
- [Timely Information Updating for Mobile Devices Without and With ML Advice](https://mlsanity.com/paper/2512.17381/) (2025)

## Abstract (arXiv)

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.