Convolutional neural networks combined with feature selection for radio-frequency fingerprinting

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.