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ISAC中面向定位隐私的子采样:基于稀疏阵列与导频的混叠欺骗

Sub-Sampling for Positioning Privacy in ISAC: Deception by Aliasing via Sparse Arrays and Pilots

L. Yashvanth, Christos Masouros, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

arXiv 2608.07206首次发表:更新:

AI 中文总结

本文针对CC-ISAC系统提出基于稀疏阵列与导频的子采样框架,通过空间-频率混叠使未授权接收方定位到虚假目标,同时不影响合法ISAC性能,实现感知与定位隐私。

AI 中文摘要

集成感知与通信(ISAC)技术可在无线系统中利用共享频谱与硬件资源同时实现通信与感知功能,但感知功能对未授权接收方的安全性仍是核心挑战。本文针对以通信为中心(CC)的ISAC系统,提出一种基于子采样的感知隐私框架,该框架联合利用稀疏阵列与稀疏导频分配,分别在空间域与频率域引入可控混叠。通过将天线阵列与导频子载波分别视为空间与频率采样机制,本文表明,空间-频率欠采样会自然地使未授权接收方观测到的距离-角多输入多输出(MIMO)模糊函数(AF)发生畸变。为此,本文首先推导了距离-角MIMO-AF的闭式表达式,随后表征了由空间与频率域混叠产生的虚假目标。接着,本文建立了这些模糊性共同转化为定位模糊的充分条件,并证明当空间与频率子采样因子足够大时,未授权接收方必然会将目标定位到错误的虚假位置。最后,本文表明所提子采样框架在不引入额外权衡的前提下,保留了合法ISAC的原生性能,数值结果验证了该分析,且稀疏阵列与稀疏导频可通过混叠欺骗自然实现感知与定位隐私。

英文摘要

Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing.

论文原文

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