A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramer-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling of ISAC waveform and STARS coefficient in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived; 2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle coupled phase-shift constants by alternatively optimizing amplitude and phase-shift coefficients in closed-form. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms the conventional RIS in CRB under the communication constraints; 2) The coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) It is more efficient to increase the number of passive elements of STARS rather than the active elements of the sensor; 4) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.

翻译：本文提出了一种基于同时透射与反射智能表面（STARS）的集成感知与通信（ISAC）框架，其中整个空间被STARS划分为感知空间与通信空间。针对感知过程中存在的显著路径损耗与杂波干扰问题，提出了一种新型"传感-STARS"结构，即在STARS上集成专用传感器。推导了感知目标二维到达角（DOAs）估计的克拉美-罗界（CRB），并在满足最低通信需求的约束下对该界进行最小化。提出了一种将复杂CRB最小化问题转化为可解修正费舍尔信息矩阵（FIM）优化问题的新方法。分别研究了STARS的独立相移与耦合相移两种模型：1) 针对独立相移模型，为解耦修正FIM中ISAC波形与STARS系数的耦合关系，基于罚对偶分解（PDD）框架设计了高效双循环迭代算法；2) 针对耦合相移模型，基于PDD框架提出了一种低复杂度交替优化算法，通过交替优化幅度系数与相移系数的闭式解来解耦耦合相移常数。数值结果表明：1) 在通信约束下，STARS在CRB性能上显著优于传统RIS；2) 当通信需求较低或STARS单元数量充足时，耦合相移模型可达到与独立模型相近的性能；3) 增加STARS无源单元数量比增加传感器有源单元数量更为高效；4) 基于实际二维最大似然估计器，STARS相比传统RIS能够实现更高的感知精度。