Online Power Control Optimization for Wireless Transmission with Energy Harvesting and Storage

TL;DR

This paper presents an online power control strategy for energy-harvesting wireless systems that maximizes long-term transmission rates without prior knowledge of energy or channel statistics, using Lyapunov optimization techniques.

Contribution

It introduces a novel Lyapunov-based online power control algorithm that handles unbounded fading and finite battery constraints, providing a closed-form, adaptive solution.

Findings

Significant performance improvement over existing online methods.

Effective handling of unbounded fading channels.

Performance bounds close to the optimal solution.

Abstract

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.