带流体智能超表面的无小区集成感知与通信（ISAC）系统的克拉美-罗界分析
Cramér-Rao Bound Analysis for Cell-Free ISAC Systems with Fluid Intelligent Metasurfaces
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中文总结 AI 辅助
本文针对带流体智能超表面的无小区ISAC系统，推导了定位CRB，揭示了无小区特有现象，提出联合优化算法，可显著降低定位CRB并保障通信服务质量。
中文摘要 AI 辅助
流体智能超表面（Fluid Intelligent Metasurface, FIM）是一种新兴的天线架构，可通过持续重塑自身物理几何结构来优化无线性能。现有关于FIM辅助的集成感知与通信（Integrated Sensing and Communication, ISAC）的研究多依赖共址单基站（Base Station, BS）部署，受限于观测角度，这些研究根本上未能充分利用FIM的形态灵活性。本文研究FIM增强的无小区ISAC架构，其中分布式接入点（Access Point, AP）从不同角度协同观测目标。我们推导了用于目标角度估计的完整费舍尔信息矩阵，并获得了明确量化角度分集增益的闭式定位克拉美-罗界（CRB）。通过分析费舍尔信息矩阵的块结构，我们揭示了三种无小区特有的现象：（i）跨AP信息耦合；（ii）发射端-接收端FIM的乘性耦合；（iii）角度分集放大。在28GHz配置下，当每个AP配备4个AP和8个FIM单元时，我们的分析表明，分布式角度分集将FIM的形态增益放大至15.8dB，而在总天线数相同的单AP对部署中，该增益仅为0.4dB。我们进一步提出了一种交替优化算法，用于通过半定松弛实现联合波束成形和FIM形状设计，该算法的紧致性已得到形式化证明。数值结果证实，所提出的无小区FIM-ISAC架构在10dB感知信噪比（SNR）下，与单AP固定阵列基线相比，定位CRB降低了4.5dB，同时在整个帕累托前沿维持了通信服务质量约束。
英文摘要
Fluid intelligent metasurface (FIM) is an emerging antenna architecture that continuously reshapes its physical geometry to optimize wireless performance. While existing studies on FIM-aided integrated sensing and communication (ISAC) rely on co-located single-base-station (BS) deployments, they fundamentally underutilize FIM's morphological flexibility due to restricted observation angles. In this paper, we investigate a FIM-augmented cell-free ISAC architecture, where distributed access points (APs) collaboratively observe a target from diverse angles. We derive the complete Fisher information matrix for target angle estimation and obtain a closed-form localization CRB that explicitly quantifies the angular diversity gain. By analyzing the block structure of the Fisher information matrix, we uncover three cell-free-specific phenomena: (i) cross-AP information coupling, (ii) multiplicative Tx--Rx FIM coupling, and (iii) angular diversity amplification. Under a 28\,GHz configuration with four APs and eight FIM elements per AP, our analysis shows that distributed angular diversity amplifies the FIM morphing gain to 15.8\,dB, compared to only 0.4\,dB in a single-AP pair deployment with the same total antenna count. We further propose an alternating optimization algorithm for joint beamforming and FIM shape design via semidefinite relaxation whose tightness is formally proved. Numerical results confirm that the proposed cell-free FIM-ISAC architecture achieves a 4.5\,dB localization CRB reduction over the single-AP fixed-array baseline at 10\,dB sensing SNR while maintaining communication quality-of-service constraints across the entire Pareto frontier.
