The cell-to-cell coupling in a reconfigurable intelligent surface (RIS) is very different from a periodic structure, where coupling effects can be precisely evaluated via full-wave analysis with periodic boundary conditions. We propose a novel method based on convolutional neural networks (CNNs), to predict the contribution of mutual coupling on the near-zone tangential electric field of every RIS unit cell that characterizes its scattering. Our CNN model incorporates an attention mechanism based on the squeeze-and-excitation block module, enhancing its capability to discern and quantify coupling effects, especially from neighboring cells surrounding the unit cell of interest. The predictions of the model enable the computation of RIS scattered fields, fully accounting for the aperiodic nature of an RIS. Comparisons to finite-element analysis confirm that our computed fields are accurate at any point and for any RIS configuration. Furthermore, our trained model can be retrained through transfer learning, to accurately and efficiently predict cell-to-cell coupling under different incident wave conditions, utilizing only a reduced number of training data. Therefore, the proposed method is a valuable tool for various practical applications, such as synthesizing RIS scattered field patterns and evaluating the performance of RIS-enabled channels.

Most existing methods for analyzing reconfigurable intelligent surface (RIS) channels are limited to free-space links and do not account for the mutual coupling of unit cells, or they fail to accurately model several types of realistic scenarios including near-field operation and interaction of RISs with rich multipath environments. Alternatively, some methods that can handle these scenarios are computationally intensive, such as pure full-wave analysis. We present an equivalence principle-based hybrid ray-tracing/full-wave method to model wave propagation in wireless communication channels enabled by RISs. This method uses ray-tracing to determine the incident waves on an RIS, then applies full-wave analysis to model the response of the RIS to these waves. Next, based on the equivalence principle, we introduce equivalent surface electric and magnetic current densities that generate the scattered fields produced by RIS. These equivalent sources are integrated with ray-tracing to derive the site-specific propagation model of the RIS channel. The proposed method readily accounts for multiple incident waves on an RIS, enabling the accurate analysis of propagation channels with RISs, with receivers in both the near and the far region of an RIS. We show that the accuracy of our method is comparable to that of full-wave analysis, through simple examples that are manageable by the finite-element method. Also, we experimentally validate the proposed technique by comparing simulation and measured data for an actual indoor radio environment with an anomalous reflection metasurface.

We propose a method to efficiently compute the scattered electric field of a reconfigurable intelligent surface (RIS) for multiple configurations. In contrast to most existing methods that assume that each unit cell scatters an incident wave individually instead of collectively, our method accounts for the mutual coupling of unit cells. This allows us to estimate the scattered fields in the main scattering direction of an RIS, at an accuracy that is comparable to full-wave analysis. Furthermore, combined with ray-tracing, the computed scattered fields can be used to model wave propagation in realistic, multipath radio environments with RISs. Hence, our method efficiently addresses three critical considerations for the analysis of RIS-enabled links: mutual coupling between unit cells of an RIS, mutlipath effects in the channel due to the RIS acting as a diffuse scatterer, and the variability of the RIS scattering properties that requires extensive computational effort to account for.

We present a hybrid numerical method that combines full-wave analysis with ray-tracing to efficiently model propagation in reconfigurable intelligent surface (RIS)-enabled communication channels. Full-wave simulation is used to obtain the radiation pattern of the transmitter and the complex radar cross-section (RCS) of the RIS. Then, the RIS is imported into a ray-tracer as a secondary transmitter. That enables the modeling of the interaction of the RIS with complex environments. We present the formulation of the proposed technique and its validation against full-wave (finite element) results in representative scenarios of indoor propagation in the presence of an RIS.