Channel Estimation for Reconfigurable Intelligent Surface Assisted Upper Mid-Band MIMO Systems

TL;DR

This paper introduces a novel conditioning-aware channel estimation framework for RIS-assisted UMB MIMO systems, effectively addressing near-field and transitional scattering challenges to improve estimation accuracy and stability.

Contribution

It proposes a greedy column grouping approach that transforms ill-conditioned high-dimensional problems into well-conditioned subproblems, enhancing channel estimation without sparsity assumptions.

Findings

Outperforms conventional LS and OMP estimators in simulations

Achieves robust performance with low computational complexity

Effectively handles near-field and transitional scattering effects

Abstract

The upper mid-band (UMB) spectrum is a key enabler for 6G systems, yet reconfigurable intelligent surface (RIS)-assisted UMB communications face severe channel estimation challenges due to near-field propagation and transitional scattering, which induce strong spatial correlation and ill-conditioned least-squares (LS) formulations. To overcome this limitation, we propose a conditioning-aware channel estimation framework that transforms the inherently ill-conditioned high-dimensional problem into multiple well-conditioned subproblems via greedy column grouping. By systematically separating highly correlated RIS elements into distinct sub-blocks via piecewise RIS phase design, the proposed method directly improves Gram matrix conditioning and stabilizes piecewise LS reconstruction without relying on sparsity assumptions. Simulation results demonstrate that the proposed method significantly outperforms conventional LS and OMP-based estimators in pilot-limited and transitional UMB regimes, achieving robust performance with low computational complexity.