Radio-frequency fingerprinting is a technique for the authentication and identification of wireless devices using their intrinsic physical features and an analysis of the digitized signal collected during transmission. The technique is based on the fact that the unique physical features of the devices generate discriminating features in the transmitted signal, which can then be analyzed using signal-processing and machine-learning algorithms. Deep learning and more specifically convolutional neural networks (CNNs) have been successfully applied to the problem of radio-frequency fingerprinting using a spectral domain representation of the signal. A potential problem is the large size of the data to be processed, because this size impacts on the processing time during the application of the CNN. We propose an approach to addressing this problem, based on dimensionality reduction using feature-selection algorithms before the spectrum domain representation is given as an input to the CNN. The approach is applied to two public data sets of radio-frequency devices using different feature-selection algorithms for different values of the signal-to-noise ratio. The results show that the approach is able to achieve not only a shorter processing time; it also provides a superior classification performance in comparison to the direct application of CNNs.

中文翻译：

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卷积神经网络与射频指纹特征选择相结合

射频指纹识别是一种利用无线设备固有的物理特征以及对传输过程中收集的数字化信号进行分析来验证和识别无线设备的技术。该技术基于这样一个事实：设备独特的物理特征会在传输信号中产生区分特征，然后可以使用信号处理和机器学习算法来分析这些特征。深度学习，更具体地说，卷积神经网络 (CNN) 已成功应用于使用信号的谱域表示的射频指纹识别问题。一个潜在的问题是要处理的数据量很大，因为这个数据量会影响CNN应用过程中的处理时间。我们提出了一种解决此问题的方法，该方法基于在将谱域表示作为 CNN 的输入给出之前使用特征选择算法进行降维。该方法应用于射频设备的两个公共数据集，针对不同的信噪比值使用不同的特征选择算法。结果表明，该方法不仅能够实现更短的处理时间，而且能够实现更短的处理时间。与直接应用 CNN 相比，它还提供了卓越的分类性能。