Intelligent autoencoder for space-time-coding digital metasurfaces

Abstract

Space-time-coding (STC) digital metasurfaces provide a powerful platform for simultaneous spatiotemporal modulations of electromagnetic waves. Therefore, the fast and accurate generation of STC matrices based on desired harmonic scattering patterns can help STC metasurfaces enhance their practicality in various applications. Here, we propose a physics-driven vector-quantized (PD-VQ) intelligent autoencoder model that consists of an encoder, a vector-quantizer layer, and a physics-driven decoder. The physical operation mechanism between the STC matrix and the harmonic scattering pattern is introduced into the decoding module of the PD-VQ intelligent autoencoder, so that the autoencoder can be trained in an unsupervised manner without the need for large amount of manually labeled data. Taking a target harmonic scattering pattern as input, the trained PD-VQ autoencoder can quickly output the optimized discrete STC matrix, which takes only about 78 ms. We present a series of simulation examples to verify the reliability and accuracy of the proposed approach and also demonstrate its good generalization capability. Based on the proposed PD-VQ intelligent autoencoder, the STC digital metasurfaces enable agile multi-frequency harmonic beamforming.