# Age Optimal Sampling for Unreliable Channels under Unknown Channel Statistics

arXiv: https://arxiv.org/abs/2412.18119 · HTML: https://mlsanity.com/paper/2412.18119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 24 Dec 2024.

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

- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [Momentum-Based Variance Reduction in Non-Convex SGD](https://mlsanity.com/paper/1905.10018/) (2019)
- [SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient](https://mlsanity.com/paper/1703.00102/) (2017)
- [An Improved Analysis of Stochastic Gradient Descent with Momentum](https://mlsanity.com/paper/2007.07989/) (2020)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things](https://mlsanity.com/paper/1807.04356/) (2018)
- [A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems](https://mlsanity.com/paper/1908.06367/) (2019)
- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [An Online Learning Approach to Optimizing Time-Varying Costs of AoI](https://mlsanity.com/paper/2105.13383/) (2021)
- [Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost](https://mlsanity.com/paper/1902.09467/) (2019)
- [When to Preempt? Age of Information Minimization under Link Capacity Constraint](https://mlsanity.com/paper/1812.05670/) (2018)
- [Sending Timely Status Updates through Channel with Random Delay via Online Learning](https://mlsanity.com/paper/2112.10400/) (2021)
- [Age of Information in Downlink Systems: Broadcast or Unicast Transmission?](https://mlsanity.com/paper/2210.15078/) (2022)

## Abstract (arXiv)

In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate O(ln K), where K is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the K-th successful transmissions grows with the rate at least Omega(ln K) in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.