This paper proposes a hybrid mono- and bi-static sensing framework, by leveraging the base station (BS) and user equipment (UE) cooperation in integrated sensing and communication (ISAC) systems. This scheme is built on 3GPP-supported sensing modes, and it does not incur any extra spectrum cost or inter-cell coordination. To reveal the fundamental performance limit of the proposed hybrid sensing mode, we derive closed-form Cramér-Rao lower bound (CRLB) for sensing target localization and velocity estimation, as functions of target and UE positions. The results reveal that significant performance gains can be achieved over the purely mono- or bi-static sensing, especially when the BS-target-UE form a favorable geometry, which is close to a right triangle. The analytical results are validated by simulations using effective parameter estimation algorithm and weighted mean square error (MSE) fusion method. Based on the derived sensing bound, we further analyze the sensing coverage by varying the UE positions, which shows that sensing coverage first improves then degrades as the BS-UE separation increases. Furthermore, the sensing accuracy for a potential target with best UE selection is derived as a function of the UE density in the network.

翻译：本文提出一种混合单/双静态感知框架，通过利用基站与用户设备在集成感知与通信系统中的协作实现。该方案建立在3GPP支持的感知模式基础上，且不产生额外频谱开销或跨小区协调成本。为揭示所提混合感知模式的基础性能极限，我们推导了用于目标定位与速度估计的闭式克拉美-罗下界，并将其表征为目标与用户设备位置的函数。结果表明，相较于纯单静态或双静态感知，该方案能实现显著的性能增益，尤其在基站-目标-用户设备形成接近直角三角形的有利几何构型时更为突出。通过采用有效参数估计算法与加权均方误差融合方法的仿真验证了理论分析结果。基于推导的感知性能边界，我们进一步通过改变用户设备位置分析感知覆盖范围，发现随着基站-用户设备间距增大，感知覆盖呈现先改善后恶化的趋势。此外，推导了采用最优用户设备选择策略时潜在目标的感知精度，并将其表征为网络用户设备密度的函数。