Reconfigurable intelligent surface (RIS) is a two-dimensional periodic structure integrated with a large number of reflective elements, which can manipulate electromagnetic waves in a digital way, offering great potentials for wireless communication and radar detection applications. However, conventional RIS designs highly rely on extensive full-wave EM simulations that are extremely time-consuming. To address this challenge, we propose a machine-learning-assisted approach for efficient RIS design. An accurate and fast model to predict the reflection coefficient of RIS element is developed by combining a multi-layer perceptron neural network (MLP) and a dual-port network, which can significantly reduce tedious EM simulations in the network training. A RIS has been practically designed based on the proposed method. To verify the proposed method, the RIS has also been fabricated and measured. The experimental results are in good agreement with the simulation results, which validates the efficacy of the proposed method in RIS design.

翻译：可重构智能表面（RIS）是一种集成大量反射单元的二维周期性结构，能够以数字化方式调控电磁波，在无线通信与雷达探测领域展现出巨大应用潜力。然而，传统RIS设计高度依赖耗时的全波电磁仿真。为应对这一挑战，本文提出一种机器学习辅助的高效RIS设计方法。通过结合多层感知器神经网络（MLP）与双端口网络，开发了能够准确快速预测RIS单元反射系数的模型，可显著减少网络训练中繁复的电磁仿真。基于所提方法实际设计了一款RIS，并通过加工制造与实测验证方案有效性。实验结果与仿真结果高度吻合，证实了该方法在RIS设计中的实用价值。