Envisioned as the next-generation transceiver technology, the holographic multiple-input-multiple-output (HMIMO) garners attention for its superior capabilities of fabricating electromagnetic (EM) waves. However, the densely packed antenna elements significantly increase the dimension of the HMIMO channel matrix, rendering traditional channel estimation methods inefficient. While the dimension curse can be relieved to avoid the proportional increase with the antenna density using the state-of-the-art wavenumber-domain sparse representation, the sparse recovery complexity remains tied to the order of non-zero elements in the sparse channel, which still considerably exceeds the number of scatterers. By modeling the inherent clustered sparsity using a Gaussian mixed model (GMM)-based von Mises-Fisher (vMF) distribution, the to-be-estimated channel characteristics can be compressed to the scatterer level. Upon the sparsity extraction, a novel wavenumber-domain expectation-maximization (WD-EM) algorithm is proposed to implement the cluster-by-cluster variational inference, thus significantly reducing the computational complexity. Simulation results verify the robustness of the proposed scheme across overheads and signal-to-noise ratio (SNR).

翻译：作为下一代收发器技术，全息多输入多输出（HMIMO）因其调控电磁波的卓越能力而备受关注。然而，密集排布的天线单元显著增大了HMIMO信道矩阵的维度，导致传统信道估计方法效率低下。虽然采用先进的波数域稀疏表示可缓解维度灾难，避免复杂度随天线密度成比例增长，但稀疏恢复的计算复杂度仍取决于稀疏信道中非零元素的阶数，该数值仍远超过散射体数量。通过基于高斯混合模型（GMM）的冯·米塞斯-费舍尔（vMF）分布对固有聚类稀疏性进行建模，可将待估计的信道特征压缩至散射体层级。在提取稀疏性的基础上，本文提出一种新型波数域期望最大化（WD-EM）算法，实现逐聚类变分推断，从而显著降低计算复杂度。仿真结果验证了所提方案在不同开销和信噪比（SNR）条件下的鲁棒性。