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

TL;DR

This work addresses energy-efficiency optimization in RIS-assisted multiuser downlink near-field communications under practical hardware constraints. It develops a nested optimization framework combining an outer integer-PSO search over discrete RIS phase configurations with an inner Dinkelbach-IQT-based power allocation, all under realistic power models for PIN diodes, varactor diodes, and RF switches. The approach demonstrates significant EE gains and reveals how RIS size, resolution, and reconfiguration method interact to affect SE and total power; it identifies architectures that maximize EE for indoor near-field deployments. The findings offer practical design guidelines for energy-efficient RIS-enabled networks in 6G-era indoor scenarios, highlighting when discrete-phase RISs outperform continuous-phase alternatives under real-world power considerations.

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.

Figures (9)

• Figure 1: Four different scenarios of RIS, both the incidence and reflection are in the (a) far field or (b) near field, or one is in the far field while the other is in the near field in (c) and (d).
• Figure 2: Alternative optimization framework comprised of the proposed integer-PSO-based RIS phase shift optimization and Dinkelbach algorithm-based FBS transmit power allocation.
• Figure 3: Illustration of the simulated near-field scenario, where an RIS is deployed at the top of the indoor space, while FBS and UEs are randomly distributed within the solving space $S_{BU}$.
• Figure 4: A group of convergence curves yielded by the proposed integer-based RIS near-field EE optimization algorithm, using PIN diodes, varactor diodes, and RF switches as the reconfigurable elements of the RIS.
• Figure 5: Convergence curves yielded by the proposed Dinkelbach-IQT algorithm, where (a) represents the outer iterations and (b) represents the inner iterations.