Adaptive Channel‐Robust Signal Fusion Method for Collaborative Device Fingerprint Identification

ABSTRACT Radio frequency fingerprint identification (RFFI) has become a popular research topic due to its potential ability to address security issues. However, how to efficiently leverage the diversity gain of multi‐source signals for enhancing the collaborative RFFI (Co‐RFFI) performance is still a challenging and unsolved topic, especially under realistic conditions involving unknown channel variations and unbalanced signal‐to‐noise ratios (SNRs) across receivers. To this end, this letter proposes a high‐performance Co‐RFFI method, where an adaptive channel‐robust signal fusion (ACRSF) method is designed. Specifically, we first generate the amplitude‐limited denoised spectral quotient (ALDSQ) signal on each receiver in a channel‐robust manner. Then, a novel signal‐level fusion strategy utilizes SNR‐based softmax weighting to adaptively combine these ALDSQ signals, thus prioritizing discriminative information from higher‐quality sources. At last, a well‐trained convolutional neural network (CNN) is employed to accomplish the final classification. Extensive simulation results demonstrate that the proposed Co‐RFFI method, ACRSF‐CNN, provides significant performance improvement compared to other existing fusion schemes under unknown channel‐varying environments.