IRS-Assisted RF-powered IoT Networks:System Modeling and Performance Analysis

TL;DR

This paper models and analyzes IRS-assisted RF-powered IoT networks, demonstrating how IRS passive beamforming improves coverage and throughput, and evaluating the impact of system parameters using stochastic geometry.

Contribution

It introduces an analytical framework for IRS-assisted RF-powered IoT networks, quantifying performance gains and effects of key system parameters.

Findings

IRS passive beamforming enhances energy and uplink coverage.

Optimal charging stage ratio remains unchanged with IRS deployment.

Network performance improves with increased IRS density and reflecting elements.

Abstract

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RFpowered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.