论文标题

传感辅助窃听器估计:艾萨克(ISAC)的物理层安全性突破

Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security

论文作者

Su, Nanchi, Liu, Fan, Masouros, Christos

论文摘要

在本文中,我们研究了感应辅助的物理层安全性(PLS)针对集成的传感和通信(ISAC)系统。 PLS的一个众所周知的局限性是需要了解潜在的窃听者(EVES)。 ISAC的感应功能在这里通过估计潜在eve的方向为PLS提供了支持。在我们的方法中,ISAC基站(BS)首先发出了Omni方向波形,以通过使用组合的Capon和近似最大似然(CAML)技术来搜索潜在的EVES方向。使用有关潜在eves的结果信息,我们制定了保密率表达式,这是EVES估计精度的函数。然后,我们提出一个加权优化问题,以同时最大程度地提高保密率并借助人工噪声(AN)最小化CRB,并最大程度地减少目标'/eves'估计的CRB。通过考虑可能的估计误差,我们可以使用宽阔的主梁覆盖所有可能的eves方向。这意味着必须在所有这些方向上执行安全性。通过提高估计精度,感应和安全功能可以提供互惠互利,从而随着优化的每一个迭代而改善相互表现,直到收敛为止。我们的结果利用了这些相互利益,并揭示了传感作为实践PL的推动者的有用性。

In this paper, we investigate the sensing-aided physical layer security (PLS) towards Integrated Sensing and Communication (ISAC) systems. A well-known limitation of PLS is the need to have information about potential eavesdroppers (Eves). The sensing functionality of ISAC offers an enabling role here, by estimating the directions of potential Eves to inform PLS. In our approach, the ISAC base station (BS) firstly emits an omni-directional waveform to search for potential Eves' directions by employing the combined Capon and approximate maximum likelihood (CAML) technique. Using the resulting information about potential Eves, we formulate secrecy rate expressions, that are a function of the Eves' estimation accuracy. We then formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the CRB with the aid of the artificial noise (AN), and minimize the CRB of targets'/Eves' estimation. By taking the possible estimation errors into account, we enforce a beampattern constraint with a wide main beam covering all possible directions of Eves. This implicates that security needs to be enforced in all these directions. By improving estimation accuracy, the sensing and security functionalities provide mutual benefits, resulting in improvement of the mutual performances with every iteration of the optimization, until convergence. Our results avail of these mutual benefits and reveal the usefulness of sensing as an enabler for practical PLS.

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