Modeling and Performance Analysis of Statistical Priority-Based Multiple Access: A Stochastic Geometry Approach

Zhang, Yan; He, Yu; Wang, Xijun; Sun, Hongguang; Quek, Tony Q. S.

doi:10.1109/jiot.2022.3144272

Cited by 5 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such a network, the spatial randomness of node locations, the time-varying channel fading, and the resulted complicated signal and interference distributions (caused by the reflection of IRSs) made it extremely difficult to evaluate the gain achieved by IRSs. To address the aforementioned challenges, stochastic geometry has been explored over the past few years as a powerful tool in obtaining the average spatial system-level analysis of randomly deployed wireless networks, including cellular networks, and heterogeneous networks [14][15][16][17]. With this tool, the spatial randomness of node locations are modeled by some classical point processes, such as Poisson point process (PPP), and Poisson cluster process (PCP).…”

Section: Introductionmentioning

confidence: 99%

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

Sun¹,

Zhao²,

Cheng³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%