Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
This work addresses the problem of optimal power control for energy harvesting devices under practical constraints of unknown statistics and outdated information, offering a provably near-optimal solution.
The paper proposes a learning-aided algorithm for utility optimal power control in energy harvesting devices with unknown environment statistics and outdated state information, achieving utility within O(ε) of optimal using O(1/ε) battery capacity.
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 The distribution information of the underlying wireless environment and harvestable energy is unknown and only outdated system state information is known at the device controller.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.