Cram\'er-Rao Bound Minimization for IRS-Enabled Multiuser Integrated Sensing and Communication with Extended Target

TL;DR

This paper proposes a joint beamforming optimization for IRS-enabled multiuser sensing and communication systems to minimize the Cramér-Rao bound for target estimation, enhancing sensing accuracy while maintaining communication quality.

Contribution

It introduces a novel joint optimization framework for transmit and reflective beamforming in IRS-assisted ISAC systems, considering extended targets and different receiver capabilities.

Findings

Proposed algorithms effectively minimize the CRB for target estimation.

Joint optimization outperforms benchmark schemes in sensing accuracy.

Interference pre-cancellation benefits increase with more CUs.

Abstract

This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communication (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one extended target at the non-line-of-sight (NLoS) region of the BS. The IRS is deployed to not only assist the communication from the BS to the CUs, but also enable the BS's NLoS target sensing based on the echo signals from the BS-IRS-target-IRS-BS link. To provide full degrees of freedom for sensing, we suppose that the BS sends additional dedicated sensing signals combined with the information signals. Accordingly, we consider two types of CU receivers, namely Type-I and Type-II receivers, which do not have and have the capability of cancelling the interference from the sensing signals, respectively. Under this setup, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the IRS to minimize the Cram\'er-Rao bound (CRB) for estimating the target response matrix with respect to the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs and the maximum transmit power constraint at the BS. We present efficient algorithms to solve the highly non-convex SINR-constrained CRB minimization problems, by using the techniques of alternating optimization and semi-definite relaxation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference pre-cancellation is beneficial when the number of CUs is greater than one.