Enhanced Channel Estimation for Flexible Intelligent Metasurface-Aided Communication Systems

TL;DR

This paper proposes a novel channel estimation method for flexible intelligent metasurface (FIM) systems, optimizing surface shape to improve estimation accuracy and SNR in FIM-enhanced communication.

Contribution

It introduces an optimization-based channel estimation approach for FIM systems, leveraging surface shape optimization and DOA estimation to enhance performance.

Findings

FIM significantly outperforms conventional rigid arrays in channel estimation accuracy.

Optimizing FIM surface shape reduces measurement matrix coherence.

FIM achieves notable SNR improvement in downlink MISO systems.

Abstract

Flexible intelligent metasurface (FIM) has recently received considerable interest due to its advantage in realizing a better channel condition by dynamically morphing its surface shape. An FIM consists of multiple elements deposited on a flexible substrate. These elements can not only transmit signals, but also adapt their displacements in a direction perpendicular to the FIM surface via an attached controller. In this paper, we consider the channel estimation problem for the uplink of an FIM-enhanced communication system via customizing the orthogonal matching pursuit (OMP) method. Specifically, we formulate an optimization problem of minimizing the column coherence of the measurement matrix by optimizing the FIM's surface shape, subject to the morphing range constraint. Based on the estimated direction of arrival (DOA) and channel gain, we further investigate the signal-to-noise ratio (SNR) improvement in the FIM-enhanced downlink multiple-input single-output (MISO) system. Numerical results demonstrate that an FIM significantly outperforms a conventional rigid uniform planar array (UPA), thereby showing that FIM can substantially improve channel estimation accuracy and achieve SNR improvement, even when using estimated channel parameters.