Optimal Energy Allocation for Wireless Communications With Energy Harvesting Constraints

TLDR

Energy harvesters replace fixed batteries in point‑to‑point wireless links, but their intermittent supply and the time‑selective fading channel pose a persistent challenge. The study seeks to determine the optimal energy allocation over a finite horizon that maximizes throughput while accounting for time‑varying channel conditions and harvested energy. Using dynamic programming and convex optimization, the authors analyze two side‑information scenarios—causal and full—and derive structural properties of the optimal allocation. When the battery can store unlimited energy and full side information is available, the optimal policy reduces to a staircase‑shaped water‑filling allocation.

Abstract

We consider the use of energy harvesters, in place of conventional batteries with fixed energy storage, for point-to-point wireless communications. In addition to the challenge of transmitting in a channel with time selective fading, energy harvesters provide a perpetual but unreliable energy source. In this paper, we consider the problem of energy allocation over a finite horizon, taking into account channel conditions and energy sources that are time varying, so as to maximize the throughput. Two types of side information (SI) on the channel conditions and harvested energy are assumed to be available: causal SI (of the past and present slots) or full SI (of the past, present and future slots). We obtain structural results for the optimal energy allocation, via the use of dynamic programming and convex optimization techniques. In particular, if unlimited energy can be stored in the battery with harvested energy and the full SI is available, we prove the optimality of a water-filling energy allocation solution where the so-called water levels follow a staircase function.