发表机构
Technische Universität Berlin; University of British Columbia, Kelowna; American University of Beirut (AUB); King Abdullah University of Science and Technology (KAUST)（柏林工业大学; 不列颠哥伦比亚大学（基洛纳校区）; 贝鲁特美国大学; 阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对有源可重构智能表面辅助链路，提出利用互耦结构进行低复杂度信道估计，通过压缩感知和散射矩阵结构缩减感知矩阵，在保持精度的同时显著降低复杂度。
AI 中文摘要
对有源可重构智能表面（RIS）辅助链路进行精确的信道建模和估计，对于充分发挥该技术的潜力至关重要，尤其是在元件密集集成的情况下。本研究采用了一种物理一致的模型，在RIS辅助通信中通过散射参数对互耦（MC）效应进行建模。我们将考虑互耦的信道估计问题表述为一个压缩感知（CS）问题。互耦效应导致感知矩阵维度增加，这种维度的增加显著提升了所构建压缩感知问题的复杂度。为克服这一挑战，我们提出了一种低复杂度估计器，利用散射矩阵的结构和互耦机制，获得一个尺寸缩减的设计感知矩阵。数值结果表明，我们的方法比不考虑互耦的估计器性能高出几个分贝，达到了与完全考虑互耦的解决方案相当的精度，但复杂度显著降低。
英文摘要
Accurate channel modeling and estimation of active reconfigurable intelligent surface (RIS)-assisted links with densely integrated elements are essential to fully unleashing this technology's potential. This work adopts a physically consistent model incorporating mutual coupling (MC) effects, modeled via scattering parameters, in RIS-aided communication. We formulate the MC-aware channel estimation as a compressed sensing (CS) problem. The MC effect leads to an increase in the sensing matrix dimensions. This increased dimensionality substantially elevates the complexity of the formulated CS problem. To overcome this, we propose a low-complexity estimator that leverages the structure of the scattering matrix and MC mechanisms to obtain a reduced-size design sensing matrix. Numerical results demonstrate that our approach outperforms MC-unaware estimators by several dBs, achieving accuracy comparable to fully MC-aware solutions but with significantly lower complexity.
