Channel Estimation and Passive Beamforming for Pixel-based Reconfigurable Intelligent Surfaces with Non-Separable State Response

Pixel-based reconfigurable intelligent surfaces (RISs) employ a novel design to achieve high reflection gain at a lower hardware cost by eliminating the phase shifters used in traditional RIS. However, this design presents challenges for channel estimation and passive beamforming due to its non-separable state response, rendering existing solutions ineffective. To address this, we first approximate the non-separable RIS response functions using a kernel-based method and a deep neural network, achieving high accuracy while reducing computational and memory complexity. Next, we propose a simplified cascaded channel model that focuses on dominated scattering paths with limited unknown parameters, along with customized algorithms to estimate short-term and long-term parameters separately. Finally, we introduce a low-complexity passive beamforming algorithm to configure the discrete RIS state vector, maximizing the achievable rate. Our simulation results demonstrate that the proposed solution significantly outperforms various baselines across a wide SNR range.

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

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.