Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

The radio frequency fingerprint (RFF)-based device identification is a promising physical layer authentication technique. However, the wireless channel significantly affects the RFF features of the wideband wireless devices. In this paper, we extensively investigate the impact of channel variation on RFF identification using 20 MHz IEEE 802.11 signal. A time domain least mean square (LMS) equalization-based feature extraction method has been proposed. This method progressively restores the transmitted signal and preserves more details of RFF features than the classical frequency domain equalization (FDE) method. Moreover, a hybrid identifier is proposed to take advantage of both LMS-based and FDE-based methods. With the equalized samples, a four-layer convolutional neural network is designed for device identification. An experimental system has been set up to capture the waveform of 68 802.11 devices at different positions. The experimental results show that the LMS-based method outperforms others when the acquisition positions of the training dataset are the same as those of the testing dataset. On the other hand, the FDE-based method is shown to be more effective when the acquisition positions of the training dataset do not fully include those of the testing dataset. Moreover, the hybrid identifier achieves an improvement of 2% for overall identification accuracy.