Details

Autor(en) / Beteiligte

• Chen, Youjia
• Zhang, Baoxian
• Hu, Jinsong
• Lopez-Perez, David
• Ding, Ming

Titel

Analysis on Energy Efficiency of Large Scale Intelligent Reflecting Surface-Enabled Networks

Ist Teil von

• IEEE communications letters, 2023-10, Vol.27 (10), p.1-1

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2023

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Intelligent reflecting surfaces (IRSs) have been proposed in recent years as a promising technology to enhance signal quality at high frequencies and save energy. In this paper, a Poisson bipolar network model with line segments is used to analyze the energy efficiency (EE) of an IRS-assisted, large-scale network. Specifically, we investigate the performance impact of the IRS configuration, in particular, the number of IRS elements and the phase-shifting resolution of each element. Using customized energy consumption and channel estimation models, we obtain the theoretical trade-off between signal quality and energy consumption as a function of these IRS configurations. The optimal number of elements and phase-shifting resolution of the IRS are also derived. Our results show that IRS technology has great potential for improving the EE of dense networks if their static energy consumption is small enough. Simulation results verify the accuracy of the obtained theoretical results.