Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces

TL;DR

This paper provides the first exact statistical analysis of fluid reconfigurable intelligent surfaces (FRIS), revealing their impact on channel gain, outage probability, and capacity, and demonstrating reliability improvements through spatial correlation control.

Contribution

It introduces a novel closed-form characterization of FRIS-aided channel gain under general correlation, unifying different regimes and enabling precise performance analysis.

Findings

Exact channel gain distribution as a finite sum of K-distributions.

Closed-form outage probability and ergodic capacity expressions.

Fluid reconfiguration improves reliability by altering spatial correlation.

Abstract

Fluid reconfigurable intelligent surfaces (FRIS) extend conventional RIS architectures by enabling physical reconfiguration of element positions, thereby introducing a fundamentally new degree of freedom for controlling spatial correlation and improving link reliability. Despite this promise, rigorous performance analysis of FRIS-assisted wireless systems has remained challenging, as exact statistical analyses of the end-to-end cascaded channels have been unavailable. This paper addresses this gap by providing the first exact closed-form characterization of the end-to-end cascaded channel gain in FRIS-aided systems under general spatial correlation. By exploiting the spectral structure of the FRIS-induced correlation matrix, we show that the channel gain statistics can be represented as a finite linear combination of K-distributions. This unified formulation naturally captures fully correlated, effectively decorrelated, and intrinsically uncorrelated operating regimes as special cases. Building on the derived channel statistics, we further obtain exact closed-form expressions for the outage probability and ergodic capacity. We also conduct an outage-based asymptotic analysis, which reveals the true diversity order of the system. Numerical results corroborate the proposed analytical framework via Monte Carlo simulations, benchmark its accuracy against state-of-the-art approximation-based approaches, and demonstrate that fluidic reconfiguration can yield tangible reliability gains by reshaping the spatial correlation structure.