# Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting

arXiv: https://arxiv.org/abs/2106.16037 · HTML: https://mlsanity.com/paper/2106.16037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost](https://mlsanity.com/paper/1902.09467/) (2019)
- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks](https://mlsanity.com/paper/1806.00336/) (2018)
- [Age Minimization in Energy Harvesting Communications: Energy-Controlled Delays](https://mlsanity.com/paper/1712.03945/) (2017)
- [Optimal Status Updating with a Finite-Battery Energy Harvesting Source](https://mlsanity.com/paper/1905.06679/) (2019)
- [Age-Minimal Online Policies for Energy Harvesting Sensors with Incremental Battery Recharges](https://mlsanity.com/paper/1802.02129/) (2018)
- [Age-of-Information with Information Source Diversity in an Energy Harvesting System](https://mlsanity.com/paper/2004.11135/) (2020)
- [Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning](https://mlsanity.com/paper/2004.12684/) (2020)

## Built on by (most cited first, 1 of 1)

- [Age-of-information minimization under energy harvesting and non-stationary environment](https://mlsanity.com/paper/2504.04916/) (2025)

## Abstract (arXiv)

The time average expected age of information (AoI) is studied for status updates sent over an error-prone channel from an energy-harvesting transmitter with a finite-capacity battery. Energy cost of sensing new status updates is taken into account as well as the transmission energy cost better capturing practical systems. The optimal scheduling policy is first studied under the hybrid automatic repeat request (HARQ) protocol when the channel and energy harvesting statistics are known, and the existence of a threshold-based optimal policy is shown. For the case of unknown environments, average-cost reinforcement-learning algorithms are proposed that learn the system parameters and the status update policy in real-time. The effectiveness of the proposed methods is demonstrated through numerical results.