CURE: Enabling RF Energy Harvesting using Cell-Free Massive MIMO UAVs Assisted by RIS

TL;DR

This paper introduces CURE, a framework combining cell-free massive MIMO, UAVs, and RISs to enhance RF energy harvesting for IoT sensors, aiming for sustainable and self-powered IoT networks.

Contribution

It proposes a novel integration of CFmMIMO, UAVs, and RISs for improved RF energy harvesting, validated through extensive simulations and max-min fairness optimization.

Findings

Enhanced energy harvesting performance demonstrated in simulations.

RISs significantly improve signal strength for energy transfer.

The framework achieves better fairness in energy distribution.

Abstract

The ever-evolving internet of things (IoT) has led to the growth of numerous wireless sensors, communicating through the internet infrastructure. When designing a network using these sensors, one critical aspect is the longevity and self-sustainability of these devices. For extending the lifetime of these sensors, radio frequency energy harvesting (RFEH) technology has proved to be promising. In this paper, we propose CURE, a novel framework for RFEH that effectively combines the benefits of cell-free massive MIMO (CFmMIMO), unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RISs) to provide seamless energy harvesting to IoT devices. We consider UAV as an access point (AP) in the CFmMIMO framework. To enhance the signal strength of the RFEH and information transfer, we leverage RISs owing to their passive reflection capability. Based on an extensive simulation, we validate our framework's performance by comparing the max-min fairness (MMF) algorithm for the amount of harvested energy.