Performance Analysis of Wireless Communication Systems Assisted by Fluid Reconfigurable Intelligent Surfaces

TL;DR

This work analyzes wireless systems aided by fluid reconfigurable intelligent surfaces (FRIS). It models the end-to-end FRIS channel, proving that the equivalent gain $G$ is a generalized-χ^2 form and can be accurately approximated by a Gamma distribution $G\\sim\\mathrm{Gamma}(k,\\theta)$ with $k=\\frac{(\\operatorname{tr}(\\widetilde{\\mathbf{J}}^2))^2}{\\operatorname{tr}(\\widetilde{\\mathbf{J}}^4)}$ and $\\theta=\\frac{\\operatorname{tr}(\\widetilde{\\mathbf{J}}^4)}{\\operatorname{tr}(\\widetilde{\\mathbf{J}}^2)}$, where $\\widetilde{\\mathbf{J}}=\\mathbf{S}_{M_o}\\mathbf{J}\\mathbf{S}_{M_o}^T$. Using this model, the paper derives the outage probability $P_o=F_G\\left(\\frac{\\overline{R}}{\\overline{\\gamma}L_fL_u}\\right)$ and a tight upper bound for the ergodic capacity $\\overline{C}\\le\\log_2\left(1+\\overline{\\gamma}L_fL_u\\operatorname{tr}(\\widetilde{\\mathbf{J}}^2)\\right)$, along with their high-SNR asymptotics. Numerical results show that FRIS provides substantial gains in both reliability and spectral efficiency over conventional RIS by enabling dynamic selection of active elements from a dense preset grid, with capacity gains saturating at high SNR or large activated-element counts. The framework demonstrates the practical potential of FRIS for robust and scalable future wireless networks.

Abstract

This letter investigates the performance of emerging wireless communication systems assisted by a fluid reconfigurable intelligent surface (FRIS). Unlike conventional reconfigurable intelligent surfaces (RISs), an FRIS consists of fluid-inspired metamaterials arranged in a densely packed matrix of sub-elements over a surface. It dynamically activates specific elements for signal reflection and modulation based on real-time channel conditions. Considering a downlink scenario where a base station communicates with a user terminal via a FRIS, we first characterize the statistical behavior of the equivalent end-to-end channel by deriving closed-form approximations for its cumulative distribution and probability density functions. Using these expressions, an analytical approximation for the outage probability and a tight upper bound on the ergodic capacity, including their asymptotic behaviors for high signal-to-noise ratio values, are derived. Our findings reveal key performance trends demonstrating that FRIS can substantially improve link reliability and spectral efficiency compared to conventional RISs, owing to its capability to dynamically select optimal elements from a dense preconfigured grid.

Figures (3)

• Figure 1: The considered FRIS-assisted wireless communication system with the FRIS comprising ON-OFF unit elements.
• Figure 2: Illustration of (a) the PDF and (b) the CDF of the equivalent channel gain $G$, and (c) the system configuration for $M=\left(M_x,M_z\right)=\left(20,20\right)$.
• Figure 3: (a) Outage probability performance versus the average SNR $\overline{\gamma}$ for different $M_o$, (b) ergodic capacity performance versus the average SNR $\overline{\gamma}$ for different $M_o$, and (c) ergodic capacity performance versus the number of elements $M=\left(M_x,M_z\right)$ for different $M_o$.

Theorems & Definitions (11)

• Lemma 1
• proof
• Proposition 1
• proof
• Proposition 2
• proof
• Corollary 1
• proof
• Proposition 3
• proof