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

TY - JOUR

T1 - A Joint Graph-Cut Channel Estimation Method for Multi-user Holographic MIMO

AU - Zhang, Jiaxin

AU - Shen, Wenqian

AU - Yang, Kai

AU - Gao, Zhen

AU - An, Jianping

N1 - Publisher Copyright: © 1967-2012 IEEE.

PY - 2026

Y1 - 2026

N2 - 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.

AB - 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.

KW - Holographic MIMO

KW - channel estimation

KW - compressed sensing

KW - graph cut algorithm

UR - https://www.scopus.com/pages/publications/105033119595

U2 - 10.1109/TVT.2026.3673703

DO - 10.1109/TVT.2026.3673703

M3 - Article

AN - SCOPUS:105033119595

SN - 0018-9545

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

ER -