Optimal Energy Allocation for Wireless Communications with Energy Harvesting Constraints

TL;DR

This paper studies optimal energy allocation strategies for wireless communication systems powered by energy harvesters, considering channel variability and energy constraints, using dynamic programming and convex optimization.

Contribution

It introduces a structural analysis of optimal energy allocation with causal and full side information, including a water-filling solution for unlimited storage and full SI scenarios.

Findings

Optimal policies depend on channel and energy state information.

Water-filling solution is optimal with full SI and unlimited storage.

Structural results guide energy management in energy harvesting systems.

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.