Accurate channel estimation improves active ris communications with less complexity
Exploiting Mutual Coupling Structure for Channel Estimation of Active RIS-Assisted Links
Information Theory
Summary
Getting a clear signal through technology called active reconfigurable intelligent surfaces (RIS) is tricky because parts of these devices interfere with each other. The authors found a way to estimate the communication channel more accurately by taking this interference into account. They designed a method that simplifies the complex math involved without losing accuracy. This makes signals through these devices clearer and communication better.
What this means in practice
- •For wireless network engineers: Improve signal quality in dense active RIS deployments by applying low-complexity channel estimators that account for mutual coupling effects.
- •For communication system integrators: Use the proposed method to design more efficient active RIS-assisted links with accurate channel modeling that reduces computational demands.
Authors
Simon Tarboush, Pinjun Zheng, Hadi Sarieddeen, Tareq Y. Al-Naffouri, Giuseppe Caire
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.