Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design

TL;DR

This paper introduces a machine learning approach to efficiently design reconfigurable intelligent surfaces, reducing reliance on time-consuming electromagnetic simulations by accurately predicting reflection coefficients with a neural network model.

Contribution

The paper presents a novel ML-assisted method combining neural networks and dual-port models for rapid RIS design, validated through fabrication and measurement.

Findings

The ML model accurately predicts RIS reflection coefficients.

The designed RIS matches simulation and experimental results.

The approach significantly reduces design time.

Abstract

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.