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Fluid Reconfigurable Intelligent Surfaces: Joint On-Off Selection and Beamforming with Discrete Phase Shifts

Han Xiao, Xiaoyan Hu, Kai-Kit Wong, Hanjiang Hong, George C. Alexandropoulos, Chan-Byoung Chae

TL;DR

This work introduces Fluid RIS (FRIS), a position-reconfigurable RIS realized by a dense matrix of on-off subelements, to overcome RIS limitations such as pilot overhead and doubly fading. The authors formulate a non-convex optimization problem to maximize the achievable rate $R$ by jointly selecting active FRIS ports and discrete phase shifts, and they solve it with a cross-entropy optimization (CEO) algorithm that learns probabilistic samplers for phase shifts and element activation, updating tilting parameters iteratively. The proposed CEO-based framework jointly optimizes reflection coefficients and element selection via sampling distributions, achieving notable rate gains over conventional RIS in simulations; gains grow with more ports and higher active-element counts, though with diminishing returns. The results highlight FRIS's potential to expand spatial degrees of freedom and improve energy efficiency in SU-SISO setups, offering a practical pathway for fluid, reconfigurable metasurfaces in next-generation networks.

Abstract

This letter proposes a fluid reconfigurable intelligent surface (FRIS) paradigm, extending the conventional reconfigurable intelligent surface (RIS) technology to incorporate position reconfigurability of the elements. In our model, a `fluid' element is realized by a dense matrix of subelements over a given space and dynamically selecting specific elements for signal modulation based on channel conditions. Specifically, we consider a FRIS-assisted single-user single-input single-output (SU-SISO) system and formulate an optimization problem that can jointly optimize element selection and their discrete phase shifts to maximize the achievable rate. To address this problem efficiently, we propose an iterative algorithm based on the cross-entropy optimization (CEO) framework. Simulation results reveal that FRIS achieves significant performance gains over traditional RIS.

Fluid Reconfigurable Intelligent Surfaces: Joint On-Off Selection and Beamforming with Discrete Phase Shifts

TL;DR

This work introduces Fluid RIS (FRIS), a position-reconfigurable RIS realized by a dense matrix of on-off subelements, to overcome RIS limitations such as pilot overhead and doubly fading. The authors formulate a non-convex optimization problem to maximize the achievable rate by jointly selecting active FRIS ports and discrete phase shifts, and they solve it with a cross-entropy optimization (CEO) algorithm that learns probabilistic samplers for phase shifts and element activation, updating tilting parameters iteratively. The proposed CEO-based framework jointly optimizes reflection coefficients and element selection via sampling distributions, achieving notable rate gains over conventional RIS in simulations; gains grow with more ports and higher active-element counts, though with diminishing returns. The results highlight FRIS's potential to expand spatial degrees of freedom and improve energy efficiency in SU-SISO setups, offering a practical pathway for fluid, reconfigurable metasurfaces in next-generation networks.

Abstract

This letter proposes a fluid reconfigurable intelligent surface (FRIS) paradigm, extending the conventional reconfigurable intelligent surface (RIS) technology to incorporate position reconfigurability of the elements. In our model, a `fluid' element is realized by a dense matrix of subelements over a given space and dynamically selecting specific elements for signal modulation based on channel conditions. Specifically, we consider a FRIS-assisted single-user single-input single-output (SU-SISO) system and formulate an optimization problem that can jointly optimize element selection and their discrete phase shifts to maximize the achievable rate. To address this problem efficiently, we propose an iterative algorithm based on the cross-entropy optimization (CEO) framework. Simulation results reveal that FRIS achieves significant performance gains over traditional RIS.

Paper Structure

This paper contains 11 sections, 17 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation and Algorithm Design
  4. Problem Formulation
  5. Algorithm Design
  6. Sampling distribution model for reflection coefficients
  7. Sampling distribution model for element selection
  8. Joint sampling distribution model
  9. Updating formulas for tilting parameters
  10. Simulation Results
  11. Conclusion

Figures (3)

  • Figure 1: The considered model of a FRIS-assisted wireless network.
  • Figure 2: The achievable rate versus the number of elements equipped at the RIS system considering different $\widehat{M}$ and phase-shift resolutions $b$.
  • Figure 3: The achievable rate versus $\widehat{M}$ considering different ${M}$ and $b$, as well as the optimal configuration of the FRIS under different ${M}$: (a) $(M_y, M_z)= (10, 10)$; (b) $(M_y, M_z)= (16, 16)$; (c) $(M_y, M_z)= (10, 10), \widehat{M} = 16$; (d) $(M_y, M_z)= (16, 16), \widehat{M} = 16$.