Radio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions.

中文翻译：

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瞬态信号检测方法的性能评估和能量准则 (EC) 方法的优越性

基于 RF 瞬变的射频指纹 (RFF) 是提高无线安全性的最有效技术之一。为实现高效的 RFF 开发，需要准确检测 RF 瞬变。然而，由于信道噪声，瞬态起点的检测仍然是一个主要挑战。在文献中，已经提出了几种方法来检测瞬态信号的起点。作为这些方法的替代方案，本研究首次提出了一种利用能量准则 (EC) 技术的方法。为了测试其性能，首先，创建了一个由在真实信噪比 (SNR) 条件下记录的 Wi-Fi 信号组成的广泛数据集。使用提供的数据集，所提出的方法以及常见的瞬态检测方法用于瞬态启动检测。然后，评估 SNR 对瞬态启动检测性能的影响。此外，基于它们各自的计算速度和复杂性提供了方法之间的性能比较。结果证明了所提出的方法在检测 Wi-Fi 设备识别 RFF 瞬态起点的可行性和效率。就作者的知识而言，这项研究是第一个通过在现实噪声条件下使用大量数据来比较评估瞬态检测方法的报告。基于它们各自的计算速度和复杂性提供了这些方法之间的性能比较。结果证明了所提出的方法在检测 Wi-Fi 设备识别 RFF 瞬态起点的可行性和效率。就作者的知识而言，这项研究是第一个通过在现实噪声条件下使用大量数据来比较评估瞬态检测方法的报告。基于它们各自的计算速度和复杂性提供了这些方法之间的性能比较。结果证明了所提出的方法在检测 Wi-Fi 设备识别 RFF 瞬态起点的可行性和效率。就作者的知识而言，这项研究是第一个通过在现实噪声条件下使用大量数据来比较评估瞬态检测方法的报告。