A Robust Radio Frequency Fingerprint Open-Set Recognition Scheme for IoT Devices

Radio frequency fingerprint (RFF) identification is a promising solution for Internet of Things (IoT) device authentication. However, this technique encounters practical challenges such as noise interference, channel coupling, and open-set recognition (OSR). This paper proposes a unified RFF-OSR framework to jointly address these problems in complex environments. Firstly, the framework mitigates the noise interference by employing a low-pass filter-integrated autoencoder, where the low-pass filter is used to obtain a “quasi-clean” signal as the autoencoder reference, thereby reducing the demand for ideal signals. Then, the channel influence on RFF is modeled as three types: frequency offset, phase noise, and amplitude distortion. Based on this model, parameterized channel augmentation is performed to improve the generalization ability of RFF identification in unknown channel scenarios. In terms of OSR, instead of a coarse-grained uniform probability threshold for rogue device recognition, we conduct independent similarity judgments for all legitimate classes, each with an individual threshold. It effectively reduces information loss in the feature probability transformation and increases OSR performance. Under additive white Gaussian noise (AWGN) and multipath channel conditions, our method achieves OSR accuracies of 99.37% and 97.05% in ZigBee device identification, respectively, which demonstrates the effectiveness of our approach.