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

TL;DR

This paper introduces a novel approach for channel estimation and passive beamforming in pixel-based reconfigurable intelligent surfaces that lack separable response functions, using kernel methods, neural networks, and simplified models to enhance performance.

Contribution

It proposes a kernel-based and neural network approximation for non-separable RIS responses, along with a simplified channel model and low-complexity algorithms for improved beamforming.

Findings

Significant performance improvement over baselines across SNRs

Accurate approximation of non-separable RIS responses

Effective estimation of channel parameters with reduced complexity

Abstract

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.