A Joint Graph-Cut Channel Estimation Method for Multi-user Holographic MIMO

TL;DR

This paper introduces a joint graph-cut channel estimation method for multi-user holographic MIMO systems, leveraging wavenumber domain sparsity and common scatterer clusters to improve accuracy and efficiency.

Contribution

The paper proposes a novel joint graph-cut algorithm that exploits shared support among users in the wavenumber domain for improved multi-user channel estimation in HMIMO.

Findings

Outperforms independent estimation schemes in accuracy.

Reduces pilot length needed for channel estimation.

Effectively exploits clustered sparsity and common supports.

Abstract

To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut algorithm for multi-user channel estimation in the wavenumber domain. The size of the conventional angular domain channel matrix increases with the number of antennas in densely-spaced HMIMO. Therefore, user channels are projected into the wavenumber domain via a Fourier harmonic transform, revealing their inherent clustered sparsity and exploiting common scatterer clusters among users. Subsequently, a joint graph-cut channel estimation (JGC-CE) algorithm based on multi-user common supports is designed. In each iteration, the algorithm first partitions user clusters to extract shared supports. Then for each user, it performs users' individual graph update and channel estimation to reconstruct the channel matrix. Simulation results demonstrate that the proposed method outperforms independent estimation schemes for individual users in accuracy while reducing pilot length.