Online Power Control Optimization for Wireless Transmission with Energy Harvesting and Storage
It addresses the problem of optimal power control for energy-harvesting wireless devices with finite battery storage, offering a practical online solution without requiring statistical knowledge of energy arrivals or fading channels.
This paper proposes an online power control strategy for wireless transmission with energy harvesting and storage, maximizing long-term time-averaged transmission rate under battery constraints. The algorithm achieves significant performance gains over alternative online approaches in simulations.
We consider wireless transmission over fading channel powered by energy harvesting and storage devices. Assuming a finite battery storage capacity, we design an online power control strategy aiming at maximizing the long-term time-averaged transmission rate under battery operational constraints for energy harvesting. We first formulate the stochastic optimization problem, and then develop techniques to transform this problem and employ techniques from Lyapunov optimization to design the online power control solution. In particular, we propose an approach to handle unbounded channel fade which cannot by directly dealt with by Lyapunov framework. Our proposed algorithm determines the transmission power based only on the current energy state of the battery and channel fade conditions,without requiring any knowledge of the statistics of energy arrivals and fading channels. Our online power control solution is a three-stage closed-form solution depending on the battery energy level. It not only provides strategic energy conservation through the battery energy control, but also reveals an opportunistic transmission style based on fading condition, both of which improve the long-term time-averaged transmission rate. We further characterize the performance bound of our proposed algorithm to the optimal solution with a general fading distribution. Simulation results demonstrate a significant performance gain of our proposed online algorithm over alternative online approaches.