Abstract
The lack of labelled signal datasets in noncooperative scenarios limits the performance of specific emitter identification (SEI). To address this limitation, a method for SEI with limited labelled signals is proposed. The bispectrum of the received signal is estimated to enhance individual discriminability. An information-maximising generative adversarial network (InfoGAN) is then developed to perform SEI with limited labelled signals. To prevent nonconvergence and mode collapse due to the complexity of the radiofrequency signals, we improve the InfoGAN, respectively, from the generator and discriminator perspective. For the former, an encoder is combined with the InfoGAN generator to form a variational autoencoder that reduces the difficulty of convergence during training. For the latter, a gradient penalty algorithm is applied during the training of the InfoGAN discriminator, which enables its training loss function to obey the 1-Lipschitz constraint, thereby avoiding gradient disappearance. The design of the objective function for the training of each subnetwork and the training procedure are provided. The proposed network is trained with limited labelled and abundant unlabelled data, and an auxiliary classifier categorizes the emitters after training. Numerical results indicate that our method outperforms state-of-the-art algorithms for SEI with limited labelled signal samples in terms of effectiveness, convergence, accuracy, and robustness against noise.
Highlights
Specific emitter identification (SEI), as one of such key technologies, enables the identification of individual sources of radiofrequency (RF) signals based on the RF fingerprints (RFFs) that result from the nonideal hardware tolerances of the emitters [4]
The remaining USRP device was connected to the same laptop and served as a receiver to collect the seven classes of RF signals generated by the other devices
Bispectrum analysis was performed as a preprocessing method on labelled and unlabelled RF signal data to obtain RFF representation data, which was fed to the network model information-maximising generative adversarial network (InfoGAN) for semisupervised training and emitter identification
Summary
With the advent of the 5G era, the demand for radio spectrum has increased significantly. Specific emitter identification (SEI), as one of such key technologies, enables the identification of individual sources of radiofrequency (RF) signals based on the RF fingerprints (RFFs) that result from the nonideal hardware tolerances of the emitters [4]. The RFFs extracted from the RF signals produced by a particular emitter contain unique characteristics that enable SEI to be implemented [5, 6]. The method shows an excellent performance in terms of classification accuracy and robustness when applied for SEI of Zigbee devices. Padilla et al [8] successfully identified 28 Wi-Fi devices with an accuracy of more than 95% by analysing the preamble information in the communication. This method is limited to signals with a communication preamble.
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