Wireless Power Transfer Under Kullback-Leibler Distribution Uncertainty: A Mathematical Framework

TL;DR

This paper develops a mathematical framework to analyze the worst-case performance of wireless power transfer systems under distribution uncertainty modeled by Kullback-Leibler divergence, revealing significant performance degradation with increased uncertainty.

Contribution

It introduces a convex optimization approach to quantify the impact of distribution uncertainty on energy harvesting performance in wireless power transfer.

Findings

Performance degrades with increased uncertainty levels.

Closed-form expressions for worst-case harvested energy are derived.

The framework accounts for practical nonidealities in energy harvesting.

Abstract

In this letter, we study the performance of a wireless power transfer system under energy harvesting distribution uncertainty. The uncertainty captures practical nonidealities of the rectification process and is modelled as the maximum Kullback-Leibler distance of the actual distribution from a nominal distribution. The case where symmetrized divergence is considered for the statistical distance between distributions is also considered. By formulating a convex optimization problem, we investigate a mathematical framework that provides closed form expressions for the minimum average harvested energy (the worst performance) and the associated statistical distribution. Theoretical results show that the energy harvesting performance is significantly degraded as the level of the uncertainty increases.