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Understanding the Role of Phase and Position Design in Fluid Reconfigurable Intelligent Surfaces

J. D. Vega-Sánchez, V. H. Garzón Pacheco, N. V. Orozco Garzón, H. R. Carvajal Mora, F. J. López-Martínez

TL;DR

This work analyzes Fluid Reconfigurable Intelligent Surfaces (FRIS) by separating gains from spatial mobility and phase/aperture design. It introduces an EPSO-based method to optimally place FRIS elements and a distributed BF/PS optimization to maximize end-to-end SNR, validated against conventional and compact RIS. Key findings show that spatial position optimization yields noticeable gains when PS design is absent, but those gains vanish under fully optimized BF and PS, while FRIS still outperforms compact RIS due to spatial correlation and larger effective aperture. The results guide when FRIS spatial flexibility is advantageous and quantify its benefits relative to RIS deployments, using an unsupervised Nakagami-m mixture model to tractably approximate the end-to-end channel and outage probability.

Abstract

Fluid Reconfigurable Intelligent Surfaces (FRISs) are gaining momentum as an improved alternative over classical RIS. However, it remains unclear whether their performance gains can be entirely attributed to spatial flexibility, or instead to differences in equivalent aperture or phase design. In this work, we shed light onto this problem by benchmarking FRIS vs. RIS performances in two practical scenarios: conventional RIS (same number of active elements and same overall aperture) and compact RIS (same number of active elements, and smaller aperture with sub-λ inter-element spacing). Statistical analysis demonstrates that: (i) spatial position optimization in FRIS provides noticeable gains over conventional RIS in the absence of phase-shift design; (ii) such benefits vanish when FRIS and conventional RIS employ optimal beamforming (BF) and phase shift (PS) design, making position optimization irrelevant; (iii) FRIS consistently outperforms compact RIS with optimized BF and PS design, owing to spatial correlation and smaller aperture.

Understanding the Role of Phase and Position Design in Fluid Reconfigurable Intelligent Surfaces

TL;DR

This work analyzes Fluid Reconfigurable Intelligent Surfaces (FRIS) by separating gains from spatial mobility and phase/aperture design. It introduces an EPSO-based method to optimally place FRIS elements and a distributed BF/PS optimization to maximize end-to-end SNR, validated against conventional and compact RIS. Key findings show that spatial position optimization yields noticeable gains when PS design is absent, but those gains vanish under fully optimized BF and PS, while FRIS still outperforms compact RIS due to spatial correlation and larger effective aperture. The results guide when FRIS spatial flexibility is advantageous and quantify its benefits relative to RIS deployments, using an unsupervised Nakagami-m mixture model to tractably approximate the end-to-end channel and outage probability.

Abstract

Fluid Reconfigurable Intelligent Surfaces (FRISs) are gaining momentum as an improved alternative over classical RIS. However, it remains unclear whether their performance gains can be entirely attributed to spatial flexibility, or instead to differences in equivalent aperture or phase design. In this work, we shed light onto this problem by benchmarking FRIS vs. RIS performances in two practical scenarios: conventional RIS (same number of active elements and same overall aperture) and compact RIS (same number of active elements, and smaller aperture with sub-λ inter-element spacing). Statistical analysis demonstrates that: (i) spatial position optimization in FRIS provides noticeable gains over conventional RIS in the absence of phase-shift design; (ii) such benefits vanish when FRIS and conventional RIS employ optimal beamforming (BF) and phase shift (PS) design, making position optimization irrelevant; (iii) FRIS consistently outperforms compact RIS with optimized BF and PS design, owing to spatial correlation and smaller aperture.

Paper Structure

This paper contains 12 sections, 1 theorem, 11 equations, 3 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System and Channel Models
  3. Problem Formulation and Algorithm Design
  4. Problem Formulation
  5. Optimization Algorithm Design
  6. Evolutionary Particle Swarm Optimization for Position Selection
  7. Distributed Phase Shift and Beamforming Optimization
  8. Performance Analysis
  9. Outage Probability
  10. Statistical Approximation
  11. Numerical results and discussions
  12. Conclusions

Key Result

Proposition 1

The approximate OP expression of the FRIS system is given by

Figures (3)

  • Figure 1: System model for FRIS-enabled communication.
  • Figure 2: Spatial configuration and OP achieved for FRIS-aided Multiple-Input Single-Output (MISO). In all figures, the dashed lines represent analytical solutions, while markers correspond to MC simulations. Also, for a fair comparison, the RIS is configured with the same number of elements $M$ as the FRIS in all cases.
  • Figure 3: OP vs. $\overline{\gamma}$ for $L=2$, and $M \in \{4,36,196\}$ fluid elements. Markers denote MC simulations whereas the solid lines represent analytical solutions.

Theorems & Definitions (2)

  • Proposition 1
  • proof