Amplitude-Based Sequential Optimization of Energy Harvesting with Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces an amplitude-based sequential optimization algorithm for energy harvesting in reconfigurable intelligent surfaces, enabling self-sustaining operation without external power sources by maximizing received power through phase alignment.

Contribution

It proposes a novel sequential phase-alignment algorithm that maximizes energy harvesting in RISs without coordination with RF sources, with proven convergence and improved efficiency.

Findings

Algorithm converges to optimal solution in few iterations

Outperforms random phase update in measurement efficiency

Applicable to both direct and indirect energy harvesting scenarios

Abstract

Reconfigurable Intelligent Surfaces (RISs) have gained immense popularity in recent years because of their ability to improve wireless coverage and their flexibility to adapt to the changes in a wireless environment. These advantages are due to RISs' ability to control and manipulate radio frequency (RF) wave propagation. RISs may be deployed in inaccessible locations where it is difficult or expensive to connect to the power grid. Energy harvesting can enable the RIS to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We consider both direct and indirect energy harvesting scenarios and show that the same mathematical model applies to them. We propose a sequential phase-alignment algorithm that maximizes the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value under specific circumstances. Our simulation results show that the proposed algorithm converges to the optimal solution in a few iterations and outperforms the random phase update method in terms of the number of required measurements.