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arXiv 2606.03372eess.SP
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面向安全集成感知与通信的瞬时风险最小化

Instantaneous Risk Minimization for Secure Integrated Sensing and Communication

Tsinghua University(清华大学);Beijing Union University(北京联合大学)

Chao Ge, Na Zhao, Yuan Shen

AI总结:

针对安全集成感知与通信系统,提出一种鲁棒波束成形框架,通过最小化瞬时信息泄露风险来保证最坏情况下的物理层安全,并开发了分层迭代算法解决非凸优化问题。

摘要:

为了确保最坏情况下的物理层安全,本文提出了一种面向安全集成感知与通信(ISAC)系统的鲁棒波束成形框架。与专注于最大化遍历保密率的传统设计不同,所提方法旨在最小化瞬时信息泄露风险。我们构建了一个多目标优化问题,联合抑制最坏情况下窃听者的信干噪比(SINR)、提高感知精度并保证合法用户的服务质量(QoS)。为解决由此产生的非凸问题,我们开发了一种分层迭代算法,其中外层循环根据更新的感知性能细化连续不确定性区域,内层循环在细化后的不确定性区域下优化波束成形。理论分析和仿真结果表明,所提方法以实际复杂度实现了每传输时刻的安全保障。

英文摘要:

To ensure worst-case physical layer security, this paper proposes a robust beamforming framework for secure integrated sensing and communication (ISAC) systems. Different from conventional designs that focus on maximizing the ergodic secrecy rate, the proposed method aims to minimize instantaneous information leakage risk. We formulate a multi-objective optimization problem that jointly suppresses the worst-case eavesdropper signal-to-interference-plus-noise ratio (SINR), improving sensing accuracy, and ensuring the quality of service (QoS) for legitimate users. To address the resulting non-convex problem, we develop a hierarchical iterative algorithm, in which the outer loop refines the continuous uncertainty regions based on the updated sensing performance, and the inner loop optimizes beamforming under the refined uncertainty regions. Theoretical analysis and simulation results demonstrate that the proposed method achieves per-transmission security guarantees with practical complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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