Reradiation and Scattering from a Reconfigurable Intelligent Surface: A General Macroscopic Model

TL;DR

This paper introduces a comprehensive macroscopic model for the electromagnetic scattering from reconfigurable intelligent surfaces (RISs), enabling more accurate simulation of their wave reradiation effects in wireless communication environments.

Contribution

It presents a novel, physically consistent framework that decomposes RIS reradiation into multiple scattering contributions, enhancing existing ray-based models to include anomalous reradiation effects.

Findings

Model accurately matches analytical, simulation, and measurement data.

Enables integration of RIS effects into ray-based propagation models.

Supports multiple reradiation modes through power conservation.

Abstract

Reconfigurable Intelligent Surfaces (RISs) have attracted major attention in the last few years, thanks to their useful characteristics. An RIS is a nearly passive thin surface that can dynamically change the reradiated field, and can therefore realize anomalous reflection, refraction, focalization, or other wave transformations for engineering the radio propagation environment or realizing novel surface-type antennas. Evaluating the performance and optimizing the deployment of RISs in wireless networks need physically consistent frameworks that account for the electromagnetic characteristics of dynamic metasurfaces. In this paper, we introduce a general macroscopic model for evaluating the scattering from an RIS. The proposed method decomposes the wave reradiated from an RIS into multiple scattering contributions and is aimed at being embedded into ray-based models. Since state-of-the-art ray-based models can already efficiently simulate specular wave reflection, diffraction, and diffuse scattering, but not anomalous reradiation, we enhance them with an approach based on Huygens' principle and propose two possible implementations for it. Multiple reradiation modes can be modeled through the proposed approach, using the power conservation principle. We validate the accuracy of the proposed model by benchmarking it against several case studies available in the literature, which are based on analytical models, full-wave simulations, and measurements.