# Near Optimal Online Distortion Minimization for Energy Harvesting Nodes

## TL;DR

This paper develops near-optimal online scheduling policies for energy harvesting sensors to minimize long-term source distortion, accounting for sampling costs and battery constraints, using fixed fraction strategies.

## Contribution

It introduces fixed fraction policies that are proven to be near-optimal for energy harvesting communication systems with and without sampling costs.

## Key findings

- Fixed fraction policies achieve near-optimal long-term distortion.
- The policies are effective across various energy arrival and battery scenarios.
- Bursty transmission strategies are optimal when sampling costs are considered.

## Abstract

We consider online scheduling for an energy harvesting communication system where a sensor node collects samples from a Gaussian source and sends them to a destination node over a Gaussian channel. The sensor is equipped with a finite-sized battery that is recharged by an independent and identically distributed (i.i.d.) energy harvesting process over time. The goal is to minimize the long term average distortion of the source samples received at the destination. We study two problems: the first is when sampling is cost-free, and the second is when there is a sampling cost incurred whenever samples are collected. We show that fixed fraction policies [Shaviv-Ozgur], in which a fixed fraction of the battery state is consumed in each time slot, are near-optimal in the sense that they achieve a long term average distortion that lies within a constant additive gap from the optimal solution for all energy arrivals and battery sizes. For the problem with sampling costs, the transmission policy is bursty; the sensor can collect samples and transmit for only a portion of the time.