Metasurface-based reconfigurable intelligent surfaces are able to actively control the reflection and refraction patterns on a surface to create a programmable and reconfigurable wireless propagation environment. A fast electric field integral equation formulation is developed here to model metasurface-field interactions for reconfigurable intelligent surfaces. The novelty of the formulation is that it utilizes characteristic basis functions designed specifically for approximating surface current densities on metasurfaces, thereby significantly reducing the necessary number of unknowns and decreasing the solution time compared with employing the standard Method of Moments technique with Rao-Wilton-Glisson surface basis functions. In this work, we validate the novel numerical technique and demonstrate the substantial improvement in numerical performance utilizing a two-dimensional illustrative structure.

The paper presents a Fourier technique suitable for efficiently evaluating the scattered electromagnetic (EM) field due to periodically configured reconfigurable metasurfaces (MTSs), which are the predominant realization approach of reconfigurable intelligent surfaces (RISs). The underlying analysis method, to which the Fourier technique is applied, has been recently developed by the authors for fast calculation of the EM field due to MTSs. It is also demonstrated that the efficiency of the Fourier technique can often be improved significantly when the proximity effect between neighbouring cells can be neglected. The efficacy of the technique for RIS simulation is illustrated by analyzing a reconfigurable line MTS, which is an RIS invariant in one direction.

A fast three-dimensional (3-D) numerical solver based on an electric field integral equation (EFIE) formulation is developed for analyzing metasurfaces (MTSs) in reconfigurable intelligent surface (RIS) applications. As part of the numerical model, a new methodology is introduced for the systematic generation of characteristic cell functions (CCFs). The CCFs provide a global approximation function set for the surface currents on perfect electric conductor (PEC) patches of the MTS cells with significantly fewer unknowns than regular localized expansion functions. In this work, we demonstrate the feasibility of CCFs for 3-D illustrative scenarios and present the substantial performance improvement they enable.

A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability.

The authors of the present paper have developed a fast three-dimensional numerical solver based on an electric field integral equation (EFIE) formulation to compute the scattered field of metasurfaces (MTSs). In this study, lumped elements are introduced into the previously suggested EFIE-based framework. Efficient implementation of lumped elements is essential for the rapid analysis of reconfigurable MTSs, which are among the most promising technologies for realizing reconfigurable intelligent surfaces. The lumped elements are modeled by a super element constructed as a linear combination of Rao-Wilton-Glisson functions. The extended solver allows fast re-computation of the problem for different impedance values, thus enabling numerous evaluations of the field in optimization loops. In this article, we describe the formulation, validate its implementation against commercial software, and present run-time data.