Stacked LSTM-Based Radio Frequency Fingerprint Identification With Blind Equalization
Juncheng Pan, Yingke Lei, Caiyi Lou, Fei Teng · 2024
As more and more wireless devices are widely connected to the Internet of Things(IoT), the authentication and identification technology of devices has become a key instrument in ensuring the security of IoT. The utilization of Radio Frequency Fingeprint (RFF)-based device identification technology can effectively achieve physical layer authentication of IoT devices. In this paper, a blind equalization (BE) based RFF extraction method is proposed, which can be performed during the process of receiving the signal without additional processing. A three-layer stacked long short-term memory (SLSTM) network is designed for employing BE-based RFF to identify different devices. The BE-SLSTM proposed in this paper achieves an identificaiton accuracy of 96.43% under a signal-to-noise ratio (SNR) level of 10dB and 99.36% under 30dB, as evidenced by the experimental results from 10 radio stations.