On Distributed Power Control for Uncoordinated Dual Energy Harvesting Links: Performance Bounds and Near-Optimal Policies

TL;DR

This paper develops and analyzes distributed power control policies for energy harvesting communication links without coordination, achieving near-optimal throughput bounds with minimal feedback.

Contribution

It introduces a near-optimal uncoordinated policy that asymptotically reaches the throughput upper bound with no feedback, advancing energy harvesting communication strategies.

Findings

Proposed policies achieve throughput within one bit of the theoretical upper bound.

Time-dilated policy asymptotically reaches the upper bound without feedback.

Monte Carlo simulations validate the theoretical performance of the policies.

Abstract

In this paper, we consider a point-to-point link between an energy harvesting transmitter and receiver, where neither node has the information about the battery state or energy availability at the other node. We consider a model where data is successfully delivered only in slots where both nodes are active. Energy loss occurs whenever one node turns on while the other node is in sleep mode. In each slot, based on their own energy availability, the transmitter and receiver need to independently decide whether or not to turn on, with the aim of maximizing the long-term time-average throughput. We present an upper bound on the throughput achievable by analyzing a genie-aided system that has noncausal knowledge of the energy arrivals at both the nodes. Next, we propose an online policy requiring an occasional one-bit feedback whose throughput is within one bit of the upper bound, asymptotically in the battery size. In order to further reduce the feedback required, we propose a time-dilated version of the online policy. As the time dilation gets large, this policy does not require any feedback and achieves the upper bound asymptotically in the battery size. Inspired by this, we also propose a near-optimal fully uncoordinated policy. We use Monte Carlo simulations to validate our theoretical results and illustrate the performance of the proposed policies.