RIS-Assisted Wireless Link Signatures for Specific Emitter Identification

被引:0
|
作者
Gao, Ning [1 ]
Meng, Shuchen [1 ]
Li, Cen [1 ]
Meng, Shengguo [2 ]
Tang, Wankai [2 ]
Jin, Shi [2 ]
Matthaiou, Michail [3 ]
机构
[1] Southeast Univ, Sch Cyber Sci Engn, Nanjing 210096, Peoples R China
[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
[3] Queens Univ Belfast, Ctr Wireless Innovat CWI, Belfast BT3 9DT, North Ireland
基金
美国国家科学基金会;
关键词
Integrated communications and security; integrated sensing and communications; OC-SVM; RIS; specific emitter identification; PHYSICAL-LAYER AUTHENTICATION; INTELLIGENT REFLECTING SURFACE;
D O I
10.1109/TWC.2024.3457608
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As one of the sensing tasks for integrated sensing and communications (ISAC), location distinction based specific emitter identification (SEI) plays an important role in location based services. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted SEI system, in which the legitimate emitter installs an RIS to customize the wireless link signature by controlling the ON-OFF state of RIS. Specifically, we consider the worst-case that the legitimate and a suspicious emitter are in the same spatial location. The received signal strength (RSS) of the specific emitter is adopted to analyze the feasibility of the proposed system. Then, we derive the statistical properties of this wireless link signature, and find the interesting insights about the phase-shift matrix configuration and the signal-to-noise-rate (SNR) gain, which showcase the huge potential of the proposed system on the integrated communications and security (ICAS) design in the near future. Afterwards, we derive the optimal detection threshold in the context of the presented metrics. Next, considering the acquisition difficulty of the RSS samples of the suspicious emitter, we use a one-class support vector machine (OC-SVM) to identify the specific emitter. Finally, the actual feasibility of the proposed system is verified via proof-of-concept experiments. The experiment results show that there are 76% and 99% performance improvements for the test statistic based and the OC-SVM based RIS-assisted SEI, respectively.
引用
收藏
页码:17872 / 17883
页数:12
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