Beyond Diagonal Reconfigurable Intelligent Surface Enabled Sensing: Cramer-Rao Bound Optimization

TL;DR

This paper investigates the use of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) for sensing, deriving the Cramer-Rao bound for angle-of-arrival estimation and proposing an optimization algorithm to enhance localization accuracy.

Contribution

It introduces a CRB derivation for BD-RIS in sensing and develops an adaptive Riemannian steepest ascent algorithm to optimize the scattering matrix under unitary constraints.

Findings

BD-RIS improves sensing performance over conventional RIS.

The proposed optimization scheme effectively minimizes the CRB.

Numerical results confirm superior target localization accuracy.

Abstract

Recently, beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as a more flexible solution to engineer the wireless propagation channels, thanks to its non-diagonal reflecting matrix. Although the gain of the BD-RIS over the conventional RIS in communication has been revealed in many works, its gain in 6G sensing is still unknown. This motivates us to study the BD-RIS assisted sensing in this letter. Specifically, we derive the Cramer-Rao bound (CRB) for estimating the angle-of-arrival (AOA) from the target to the BD-RIS under the constraint that the BD-RIS scattering matrix is unitary. To minimize the CRB, we develop an optimization scheme based on an adaptive Riemannian steepest ascent algorithm that can satisfy the non-convex unitary constraint. Numerical results demonstrate that the proposed BD-RIS-assisted target localization method achieves superior sensing performance.