Fluid Reconfigurable Intelligent Surface with Element-Level Pattern Reconfigurability: Beamforming and Pattern Co-Design

2025-08-13

AI Summary

To address the inflexibility of conventional reconfigurable intelligent surfaces (RISs) with fixed radiation patterns ill-suited for dynamic channels, this work proposes a fluid-based reconfigurable intelligent surface (FRIS) featuring unit-level reconfigurable radiation patterns. The core innovation lies in the first realization of dynamically tunable radiation patterns at the individual fluid-unit level, enabled by spherical harmonic orthogonal decomposition (SHOD) for modeling radiation characteristics. Joint optimization of spherical harmonic coefficients and active beamforming vectors is performed under transmit power and modal energy constraints, leveraging minimum mean square error (MMSE) beamforming and the Riemannian conjugate gradient (RCG) algorithm. Simulation results under the 3GPP 38.901 channel model demonstrate that the proposed FRIS achieves 161.5% and 176.2% gains in multi-user weighted sum rate over conventional RISs with omnidirectional radiation and the 3GPP baseline, respectively—substantially enhancing signal modulation freedom and spectral efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

This paper proposes a novel pattern-reconfigurable fluid reconfigurable intelligent surface (FRIS) framework, where each fluid element can dynamically adjust its radiation pattern based on instantaneous channel conditions. To evaluate its potential, we first conduct a comparative analysis of the received signal power in point-to-point communication systems assisted by three types of surfaces: (1) the proposed pattern-reconfigurable FRIS, (2) a position-reconfigurable FRIS, and (3) a conventional RIS. Theoretical results demonstrate that the pattern-reconfigurable FRIS provides a significant advantage in modulating transmission signals compared to the other two configurations. To further study its capabilities, we extend the framework to a multiuser communication scenario. In this context, the spherical harmonics orthogonal decomposition (SHOD) method is employed to accurately model the radiation patterns of individual fluid elements, making the pattern design process more tractable. An optimization problem is then formulated with the objective of maximizing the weighted sum rate among users by jointly designing the active beamforming vectors and the spherical harmonics coefficients, subject to both transmit power and pattern energy constraints. To tackle the resulting non-convex optimization problem, we propose an iterative algorithm that alternates between a minimum mean-square error (MMSE) approach for active beamforming and a Riemannian conjugate gradient (RCG) method for updating the spherical harmonics coefficients. Simulation results show that the proposed pattern-reconfigurable FRIS significantly outperforms traditional RIS architectures based on the 3GPP 38.901 and isotropic radiation models, achieving average performance gains of 161.5% and 176.2%, respectively.

Problem

• Proposes a fluid RIS with dynamically reconfigurable radiation patterns per element
• Maximizes weighted sum rate in multiuser systems through joint beamforming optimization
• Develops iterative algorithm combining MMSE and Riemannian methods for pattern design

Innovation

• Fluid RIS with element-level pattern reconfigurability
• Spherical harmonics decomposition for radiation modeling
• Joint beamforming and pattern optimization algorithm