# Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics

arXiv: https://arxiv.org/abs/2509.18654 · HTML: https://mlsanity.com/paper/2509.18654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Minimax Regret Bounds for Reinforcement Learning](https://mlsanity.com/paper/1703.05449/) (2017)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks](https://mlsanity.com/paper/1801.01803/) (2018)
- [Age of Information: Whittle Index for Scheduling Stochastic Arrivals](https://mlsanity.com/paper/1801.03422/) (2018)
- [A Whittle Index Approach to Minimizing Functions of Age of Information](https://mlsanity.com/paper/1908.10438/) (2019)
- [A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems](https://mlsanity.com/paper/1908.06367/) (2019)
- [An Online Learning Approach to Optimizing Time-Varying Costs of AoI](https://mlsanity.com/paper/2105.13383/) (2021)
- [Online Energy-Efficient Scheduling for Timely Information Downloads in Mobile Networks](https://mlsanity.com/paper/1901.03137/) (2019)
- [Closed-Form Whittle's Index-Enabled Random Access for Timely Status Update](https://mlsanity.com/paper/1910.13871/) (2019)
- [Optimal Sampling Cost in Wireless Networks with Age of Information Constraints](https://mlsanity.com/paper/2003.02512/) (2020)
- [Correlated Age-of-Information Bandits](https://mlsanity.com/paper/2011.05032/) (2020)
- [Learning-augmented Online Minimization of Age of Information and Transmission Costs](https://mlsanity.com/paper/2403.02573/) (2024)
- [Decentralized Age-of-Information Bandits](https://mlsanity.com/paper/2009.12961/) (2020)

## Abstract (arXiv)

We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI performance at the destination. This tradeoff has been optimized in the existing literature under the assumption of having a complete knowledge of the channel statistics. In this work, we develop online learning-based algorithms with finite-time guarantees that optimize this tradeoff in the practical scenario where the channel statistics are unknown to the scheduler. In particular, when the channel statistics are known, the optimal scheduling policy is first proven to have a threshold-based structure with respect to the value of AoI (i.e., it is optimal to drop updates when the AoI value is below some threshold). This key insight was then utilized to develop the proposed learning algorithms that surprisingly achieve an order-optimal regret (i.e., O(1)) with respect to the time horizon length.