Multi-IRS-Enabled Integrated Sensing and Communications

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cramér-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.