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arXiv 2402.07259eess.SP
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用于被动无人机检测的RIS增强毫米波MIMO系统

RIS-Augmented Millimeter-Wave MIMO Systems for Passive Drone Detection

Jiguang He, Aymen Fakhreddine, George C. Alexandropoulos

AI总结:

本文提出利用现有毫米波蜂窝MIMO基站与可重构智能表面(RIS)实现无设备被动无人机检测,通过广义似然比检验验证可行性,并分析RIS部署与训练参数以提供系统设计指导。

摘要:

在过去十年中,业余无人机的数量不断增加,预计这一趋势在未来仍将持续。无人机滥用和不当行为带来的安全问题日益严峻,可能对社会产生负面影响。本文利用现有的蜂窝多输入多输出(MIMO)基站(BS)基础设施,工作在毫米波(mmWave)频段,借助部署在基站附近的一个可重构智能表面(RIS),以无设备方式实现无人机检测。我们从理论上借助广义似然比检验(GLRT)考察了无人机检测的可行性,并通过仿真验证,与无RIS系统相比,RIS的优化部署能够带来额外收益。此外,还研究了RIS训练波束、训练开销和雷达散射截面的影响,为所提出的基于蜂窝RIS的被动无人机检测系统提供理论设计指导。

英文摘要:

In the past decade, the number of amateur drones is increasing, and this trend is expected to continue in the future. The security issues brought by abuse and misconduct of drones become more and more severe and may incur a negative impact to the society. In this paper, we leverage existing cellular multiple-input multiple-output (MIMO) base station (BS) infrastructure, operating at millimeter wave (mmWave) frequency bands, for drone detection in a device-free manner with the aid of one reconfigurable intelligent surface (RIS), deployed in the proximity of the BS. We theoretically examine the feasibility of drone detection with the aid of the generalized likelihood ratio test (GLRT) and validate via simulations that, the optimized deployment of an RIS can bring added benefits compared to RIS-free systems. In addition, the effect of RIS training beams, training overhead, and radar cross section, is investigated in order to offer theoretical design guidance for the proposed cellular RIS-based passive drone detection system.

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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