Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914433051131904 |
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| author | Khazaee, Masoud de Figueiredo, Felipe A. P. de Souza, Rausley A. A. Ghadi, Farshad Rostami Wong, Kai-Kit Mendes, Luciano L. García, Fernando D. Almeida |
| author_facet | Khazaee, Masoud de Figueiredo, Felipe A. P. de Souza, Rausley A. A. Ghadi, Farshad Rostami Wong, Kai-Kit Mendes, Luciano L. García, Fernando D. Almeida |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28974 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces Khazaee, Masoud de Figueiredo, Felipe A. P. de Souza, Rausley A. A. Ghadi, Farshad Rostami Wong, Kai-Kit Mendes, Luciano L. García, Fernando D. Almeida Signal Processing 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. |
| title | Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.28974 |