# Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information

arXiv: https://arxiv.org/abs/1801.03572 · HTML: https://mlsanity.com/paper/1801.03572/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints](https://mlsanity.com/paper/2212.01689/) (2022)
- [Optimistic Safety for Online Convex Optimization with Unknown Linear Constraints](https://mlsanity.com/paper/2403.05786/) (2024)
- [Safe Online Convex Optimization with Multi-Point Feedback](https://mlsanity.com/paper/2407.11471/) (2024)
- [Safe and Efficient Online Convex Optimization with Linear Budget Constraints and Partial Feedback](https://mlsanity.com/paper/2412.03983/) (2024)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)

## Abstract (arXiv)

This paper considers utility optimal power control for energy harvesting wireless devices with a finite capacity battery. The distribution information of the underlying wireless environment and harvestable energy is unknown and only outdated system state information is known at the device controller. This scenario shares similarity with Lyapunov opportunistic optimization and online learning but is different from both. By a novel combination of Zinkevich's online gradient learning technique and the drift-plus-penalty technique from Lyapunov opportunistic optimization, this paper proposes a learning-aided algorithm that achieves utility within O(ε) of the optimal, for any desired ε>0, by using a battery with an O(1/ε) capacity. The proposed algorithm has low complexity and makes power investment decisions based on system history, without requiring knowledge of the system state or its probability distribution.