This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated multi-point (CoMP). In the CBF scheme, a BS shares channel state information (CSI) and estimates target parameters using monostatic sensing. In contrast, a BS in the CoMP scheme shares the CSI and data, allowing bistatic sensing through inter-cell reflection. We consider both block-level (BL) and symbol-level (SL) precoding schemes for both the multi-cell scenarios that are robust to channel state estimation errors. The formulated optimization problems to minimize the CRB in estimating the parameters of a target and maximize the minimum communication signal-to-interference-plus-noise-ratio (SINR) while satisfying a given total transmit power budget are non-convex. We tackle the non-convexity using a combination of semidefinite relaxation (SDR) and alternating optimization (AO) techniques. Simulations suggest that neglecting the inter-cell reflection and communication links degrades the performance of an ISAC system. The CoMP scenario employing SL precoding performs the best, whereas the BL precoding applied in the CBF scenario produces relatively high estimation error for a given minimum SINR value.

翻译：本文提出一种鲁棒预编码器设计框架，适用于跨多小区和多用户执行集成感知与通信（ISAC）的多输入单输出（MISO）系统。我们采用克拉美罗界（CRB）衡量感知性能，并推导出其在两种多小区场景（即协作波束成形（CBF）与协作多点（CoMP））下的表达式。在CBF方案中，基站共享信道状态信息（CSI），并通过单站感知估计目标参数；而在CoMP方案中，基站共享CSI与数据，从而通过小区间反射实现双站感知。我们针对这两种多小区场景，分别考虑块级（BL）和符号级（SL）预编码方案，使其对信道状态估计误差具有鲁棒性。所构建的优化问题旨在最小化目标参数估计的CRB，并最大化通信最小信干噪比（SINR），同时满足给定总发射功率预算，该问题是非凸的。我们通过结合半定松弛（SDR）与交替优化（AO）技术处理非凸性。仿真表明，忽略小区间反射与通信链路会降低ISAC系统性能。采用SL预编码的CoMP场景性能最优，而CBF场景中采用BL预编码时，对给定最小SINR值会产生相对较高的估计误差。