Radio frequency fingerprint (RFF) identification is a promising technique that exploits hardware impairments-induced features to achieve specific device identification. Among RFF features, carrier frequency offset (CFO) as a hotspot feature has received widespread attention. Since CFO is time-variant, existing research suggests compensating for its drift; however, this paper emphasizes utilizing the drift of CFO. Correspondingly, a novel RFF feature, named cyclic similarity (cyc-similarity), is proposed to depict the oscillator drift. Simply combined the cycsimilarity feature with a K-nearest neighbor (KNN) classifier, the system can achieve superior temporal and receiver generalization performance. On a public dataset of WiFi devices, the proposed method outperforms the existing methods.

Identifying non-cooperative drones has attracted much attentions in recent years, which is critical to public safety. Extensive research has demonstrated that state-of-the-art deep learning (DL) is well suited to address this problem. This paper aims to improve the performance of the DL-based radio frequency identification (RFI) systems for drones considering some major challenges faced in practical applications, including the unknown drone operating channels, the applicability of drone signal representations to the DL models, and the variation of wireless channels. Firstly, a morphological-filtering based carrier frequency offset estimation and compensation is proposed to adapt to the switchable operating channel of drones. Secondly, the applicability of typical drone signal representations revealing different signal features and typical DL models possessing different learning capabilities is investigated, in terms of classification accuracy, training time, and number of parameters. Thirdly, a data augmentation scheme is proposed to generate training signals by injecting various channel distortions, including additive white Gaussian noise, multi-path, and Doppler shift. Finally, we set up a universal software radio peripheral based experimental platform to identify 12 types of drones in a real wireless environment. Experimental results validate the improvement of the proposed schemes. The spectrogram combined with the real-valued CNN is recommended for drone classification, which achieves an accuracy of 97% with the least training time and parameters.

This letter studies a challenging physical layer security problem for downlink transmissions when facing a randomly located active eavesdropper. Artificial noise (AN) is transmitted in conjunction with a confidential signal to defend against the eavesdropper. However, the active eavesdropper with changeable location can perform both eavesdropping and malicious jamming simultaneously, which yields the secrecy outage probability as an unsuitable performance metric. In this case, a more appropriate metric, the hybrid outage probability which takes both the transmission outage probability and secrecy outage probability into consideration, is derived to evaluate the security performance. Based on the hybrid outage probability, we can optimize the power allocation to the confidential and AN signals. Furthermore, an insecure region is defined to restrict the location of the eavesdropper. Additionally, the insecure range is defined as a valuable indicator of a circular insecure region. Simulation results are provided to depict the region around receiver is insecure.

The security problem in wireless sensor networks faces severe challenges, due to the openness of the sensor network channel and the mobility and diversity of the terminals. When facing randomly located eavesdroppers, the situation is much more complex. This paper studies the security performance of a wireless sensor network where randomly located passive and active eavesdroppers are both considered. Compared to a passive eavesdropper, an active eavesdropper can perform both eavesdropping and malicious jamming simultaneously in a wireless sensor network. Based on beamforming and artificial noise (AN), we propose a practical way to defend against the eavesdropper by establishing a protecting region. An appropriate metric, the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration, is utilized to evaluate the security performance. In addition, the concept of safe transmission range is defined to evaluate the security performance. Simulation results are provided to depict the insecure region and verify the harm of the active eavesdropper to the transmission in the wireless sensor network.