A core challenge in physical-layer security is the difficulty of obtaining the channel state information (CSI) of potential eavesdroppers. The inherent sensing functionality of integrated sensing and communication (ISAC) systems offers a promising solution by enabling the estimation of key parameters, such as the eavesdropper's angles of departure (AoDs). Capitalizing on this capability, we propose a sensing-assisted secure communication scheme for a movable antenna (MA)-aided ISAC system. The scheme comprises two stages: eavesdropper AoD sensing and secure communication. In the first stage, the base station (BS) optimizes the positions of its transmit and receive MAs to enhance sensing accuracy. We derive the closed-form Cramer-Rao bound (CRB) for the estimated AoDs to fundamentally characterize how MA positions influence the estimation uncertainty. In the second stage, the BS ensures secure communication by designing a robust beamforming vector that accounts for the AoD uncertainty region and by further optimizing the transmit MAs' positions to maximize the secrecy rate. To manage the end-to-end design, we formulate a joint optimization problem. This intractable non-convex problem is decomposed into two subproblems. For the first subproblem, we develop an alternating optimization (AO) algorithm to solve the CRB minimization problem. For the second subproblem, we solve the worst-case secrecy rate maximization problem using a method based on backward induction, convex hull construction, and AO. Finally, simulation results are provided to demonstrate the significant advantages of the proposed scheme compared to various benchmarks.

翻译：物理层安全的一个核心挑战是难以获取潜在窃听者的信道状态信息（CSI）。一体化感知与通信（ISAC）系统固有的感知功能通过能够估计关键参数（例如，窃听者的离开角（AoDs））提供了一种有前景的解决方案。利用这一能力，我们为一种移动天线（MA）辅助的ISAC系统提出了一种感知辅助的安全通信方案。该方案包括两个阶段：窃听者AoD感知和安全通信。在第一阶段，基站（BS）优化其发射和接收MA的位置以提高感知精度。我们推导了估计AoD的闭式克拉美-罗界（CRB），以从根本上刻画MA位置如何影响估计不确定性。在第二阶段，BS通过设计一个考虑AoD不确定性区域的鲁棒波束成形向量，并进一步优化发射MA的位置以最大化保密率，来确保安全通信。为管理端到端设计，我们构建了一个联合优化问题。这个棘手的非凸问题被分解为两个子问题。对于第一个子问题，我们开发了一种交替优化（AO）算法来求解CRB最小化问题。对于第二个子问题，我们使用基于反向归纳、凸包构建和AO的方法来求解最坏情况下的保密率最大化问题。最后，通过仿真结果展示了所提方案相较于各种基准的显著优势。