This paper investigates an intelligent reflecting surface (IRS) enabled multiuser integrated sensing and communications (ISAC) system, which consists of one multi-antenna base station (BS), one IRS, multiple single-antenna communication users (CUs), and one 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. We consider two types of targets, namely the extended and point targets, for which the BS aims to estimate the complete target response matrix and the target direction-of-arrival (DoA) with respect to the IRS, respectively. To provide full degrees of freedom for sensing, we consider that the BS sends dedicated sensing signals in addition to the communication signals. Accordingly, we model two types of CU receivers, namely Type-I and Type-II CU receivers, which do not have and have the capability of canceling the interference from the sensing signals, respectively. Under each 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 target estimation, 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, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design achieves lower estimation CRB than other benchmark schemes, and the sensing signal interference cancellation at Type-II CU receivers is beneficial when the number of CUs is greater than one.

翻译：本文研究了一种智能反射面（IRS）赋能的多用户集成感知与通信（ISAC）系统，该系统包含一个多天线基站（BS）、一个IRS、多个单天线通信用户（CU）以及一个位于BS非视距（NLoS）区域的目标。IRS的部署不仅用于辅助BS到CU的通信，还可基于BS-IRS-目标-IRS-BS链路的回波信号实现BS对非视距目标的感知。我们考虑两类目标：扩展目标和点目标，BS分别需要估计完整的响应矩阵和目标相对于IRS的到达方向角（DoA）。为提供完整的感知自由度，我们假设BS在发送通信信号的同时也发送专用感知信号。据此建立了两类CU接收机模型：I型CU接收机不具备感知信号干扰消除能力，而II型CU接收机具备该能力。针对每种配置，我们联合优化BS的发射波束成形与IRS的反射波束成形，在满足CU最小信干噪比（SINR）约束和BS最大发射功率约束的条件下，最小化目标估计的Cramér-Rao下界（CRB）。针对高度非凸的SINR约束CRB最小化问题，我们通过交替优化、半定松弛和逐次凸近似技术提出了高效求解算法。数值结果表明，所提设计相比其他基准方案实现了更低的估计CRB，且当CU数量大于一时，II型CU接收机的感知信号干扰消除具有显著优势。