A Machine Learning Framework for the Design of STCDME Structures in RIS Applications

This article introduces a machine learning (ML) framework for the design of space-time-coding digital meta-surface elements (STCDMEs), commonly used in reconfigurable intelligent surface (RIS)-based communication. It includes inverse design, forward design, and automodeling, which quickly achieve multistate electromagnetic (EM) structure designs, e.g., STCDME. The decision tree (DT) model is chosen for use with its lightweight, fast response, and highly accurate in EM structure small-scale data modeling. In addition, we present a new sensing STCDME design method, using gap technology based on ground structure. Using the proposed framework, we successfully design a sensing STCDME with a reflection phase within 180 degrees +/- 30 degrees ranging from 6.66 to 7.3 GHz and a reflection coefficient larger than -2 dB, meeting RIS communication requirements. In the 8.27-9.5-GHz band, the structure's transmission coefficient exceeds -3 dB, achieving EM wave transmission and sensing capabilities. The proposed framework offers a novel method for STCDME design, and the resulting sensing STCDME structure can be used for RIS sensing, contributing significantly to wireless communication and sensing applications.