Experiment-based deep learning approach for power allocation with a programmable metasurface

TL;DR

This paper introduces an experiment-based deep learning method for power allocation using programmable metasurfaces, addressing the gap between simulation and real-world performance in complex environments.

Contribution

It presents a novel experimental data-driven deep learning approach for metasurface inverse design, improving adaptability and accuracy in practical wireless communication scenarios.

Findings

Experimental data enhances DNN performance in complex environments

The approach enables rapid retraining for environment changes

Potential for energy-efficient wireless communication

Abstract

Deep learning, as a highly efficient method for metasurface inverse design, commonly use simulation data to train deep neural networks (DNNs) that can map desired functionalities to proper metasurface designs. However, the assumptions and simplifications made in the simulation model may not reflect the actual behavior of a complex system, leading to suboptimal performance of the DNNs in practical scenarios. To address this issue, we propose an experiment-based deep learning approach for metasurface inverse design and demonstrate its effectiveness for power allocation in complex environments with obstacles. Enabled by the tunability of a programmable metasurface, large sets of experimental data in various configurations can be collected for DNN training. The DNN trained by experimental data can inherently incorporate complex factors and can adapt to changed environments through its on-site data-collecting and fast-retraining capability. The proposed experiment-based DNN holds the potential for intelligent and energy-efficient wireless communication in complex indoor environments.