Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

TL;DR

This paper develops optimal and near-optimal transmission policies for energy harvesting wireless nodes in fading channels, balancing throughput maximization and transmission time minimization under energy constraints.

Contribution

It introduces a directional water-filling algorithm for offline optimization and proposes stochastic dynamic programming for online policies, advancing energy-aware transmission strategies.

Findings

Directional water-filling provides optimal offline power control.

Online policies outperform naive strategies in simulations.

Near-optimal policies offer practical solutions with reduced complexity.

Abstract

Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.