Problem
Research questions and friction points this paper is trying to address.
channel estimation
active RIS
mutual coupling
compressed sensing
Exploiting Mutual Coupling Structure for Channel Estimation of Active RIS-Assisted Links
🤖 AI Summary
本文针对主动RIS辅助链路的信道估计问题，通过利用互耦合结构并将其建模为压缩感知问题，提出了一种低复杂度的估计器方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
mutual coupling
scattering parameters
compressed sensing
low-complexity estimator
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
