Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift

arXiv:2403.04036v1 Announce Type: new
Abstract: Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain …

abstract arxiv automated challenge classification cs.ai cs.lg domain eess.sp enabling environmental however identification key radio robust shift technology type unsupervised wireless