Analysis on Energy Efficiency of RIS-Assisted Multiuser Downlink Near-Field Communications

TL;DR

This paper investigates energy efficiency optimization in RIS-assisted multiuser near-field downlink communications, considering practical power models and discrete phase constraints, and proposes a nested optimization framework validated by extensive simulations.

Contribution

It introduces a nested optimization framework for energy efficiency maximization considering practical RIS element models and discrete phase constraints.

Findings

Different RIS elements significantly affect energy efficiency.

The proposed framework outperforms benchmark schemes.

Optimal RIS configurations vary with system parameters.

Abstract

In this paper, we focus on the energy efficiency (EE) optimization and analysis of reconfigurable intelligent surface (RIS)-assisted multiuser downlink near-field communications. Specifically, we conduct a comprehensive study on several key factors affecting EE performance, including the number of RIS elements, the types of reconfigurable elements, reconfiguration resolutions, and the maximum transmit power. To accurately capture the power characteristics of RISs, we adopt more practical power consumption models for three commonly used reconfigurable elements in RISs: PIN diodes, varactor diodes, and radio frequency (RF) switches. These different elements may result in RIS systems exhibiting significantly different energy efficiencies (EEs), even when their spectral efficiencies (SEs) are similar. Considering discrete phases implemented at most RISs in practice, which makes their optimization NP-hard, we develop a nested alternating optimization framework to maximize EE, consisting of an outer integer-based optimization for discrete RIS phase reconfigurations and a nested non-convex optimization for continuous transmit power allocation within each iteration. Extensive comparisons with multiple benchmark schemes validate the effectiveness and efficiency of the proposed framework. Furthermore, based on the proposed optimization method, we analyze the EE performance of RISs across different key factors and identify the optimal RIS architecture yielding the highest EE.