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FARIS: Fluid-Active-RIS

Hong-Bae Jeon

Abstract

In this paper, we introduce a new wireless paradigm termed fluid-active reconfigurable intelligent surface (FARIS) that combines fluid-based port repositioning with per-element active amplification to enhance the performance of 6G networks. To realistically characterize the hardware operation, we first develop a circuit-level abstraction of the FARIS architecture and establish a practical power consumption model that captures both the logical control/switching power of candidate ports and the direct current (DC) bias power required for active reflection. Based on this model, we establish the FARIS signal model and formulate a corresponding ergodic-rate maximization problem that jointly optimizes the active amplification-reflection vector and the discrete selection of fluid-active elements under practical hardware constraints. The problem is addressed via an alternating optimization (AO) framework, which progressively improves the rate. Complexity and convergence analyses that follow furnish deeper insight into the algorithmic operation and performance enhancement. Numerical results confirm that the proposed FARIS with the AO framework consistently outperforms conventional baselines, delivering higher rates across diverse environments, often even when using fewer active elements or a smaller physical aperture.

FARIS: Fluid-Active-RIS

Abstract

In this paper, we introduce a new wireless paradigm termed fluid-active reconfigurable intelligent surface (FARIS) that combines fluid-based port repositioning with per-element active amplification to enhance the performance of 6G networks. To realistically characterize the hardware operation, we first develop a circuit-level abstraction of the FARIS architecture and establish a practical power consumption model that captures both the logical control/switching power of candidate ports and the direct current (DC) bias power required for active reflection. Based on this model, we establish the FARIS signal model and formulate a corresponding ergodic-rate maximization problem that jointly optimizes the active amplification-reflection vector and the discrete selection of fluid-active elements under practical hardware constraints. The problem is addressed via an alternating optimization (AO) framework, which progressively improves the rate. Complexity and convergence analyses that follow furnish deeper insight into the algorithmic operation and performance enhancement. Numerical results confirm that the proposed FARIS with the AO framework consistently outperforms conventional baselines, delivering higher rates across diverse environments, often even when using fewer active elements or a smaller physical aperture.
Paper Structure (26 sections, 4 theorems, 63 equations, 10 figures, 1 table, 3 algorithms)

This paper contains 26 sections, 4 theorems, 63 equations, 10 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Proposed AO Framework
  4. Optimizing $\mathbf v ( \leftrightarrow (\boldsymbol\Phi,\mathbf G))$ for Fixed $\mathbf S_{M_o}$
  5. Update of $\mathbf V$ ($\leftrightarrow\mathbf v$) for fixed $\{y_s\}$
  6. Update of $y_s$ for fixed $\mathbf V$
  7. Monotonicity and Convergence Analysis of Algorithm \ref{['alg:AO']}
  8. Optimizing $\mathbf S_{M_o}$ for fixed $\mathbf v$
  9. Ideal Target Distribution
  10. Applying to $M_o$-constrained CEM with Iterations
  11. Overall Procedure
  12. Overall Convergence of Algorithm \ref{['alg:overall_AO_compact']}
  13. Complexity Analysis
  14. Complexity of SAA-Based Rate Evaluation
  15. Complexity of Algorithm \ref{['alg:AO']}
  16. ...and 11 more sections

Key Result

Theorem 1

Define and where $\overline{\mathbf b}$ is the element-wise conjugate of $\mathbf b$. Then the following hold:

Figures (10)

  • Figure 1: System model of FARIS-aided wireless network.
  • Figure 2: Schematic illustration of the FARIS architecture with incorporating on-off control circuits and reflection-type amplifiers for the selected elements.
  • Figure 3: Simulation setup of the FARIS-aided system.
  • Figure 4: Illustration of examples of the optimized positions when the FARIS has (a) $M_o=16$ (b) $M_o=36$ (c) $M_o=81$ fluid active elements.
  • Figure 5: CDF of ergodic rate gap between BFS and proposed AO frameworks for different $M_o$ with $M=16$.
  • ...and 5 more figures

Theorems & Definitions (9)

  • Theorem 1
  • proof
  • Remark 1
  • Theorem 2
  • proof
  • Theorem 3
  • proof
  • Theorem 4
  • proof