Self-Robust RF Fingerprint Identification With 6G Agentic AI: Cloud-Edge Intelligent Collaboration

The rapid proliferation of heterogeneous wireless devices in sixth-generation (6G) networks raises significant security challenges, necessitating continuous and zero-trust authentication. Radio frequency fingerprint identification (RFFI) is a promising solution, however, its performance deteriorates markedly under dynamic and unpredictable wireless environments. To address this issue, we propose DrffNet-X, a cross-modal RFF distillation framework to train a lightweight and self-robust RFFI model within a cloud-edge collaborative agentic artificial intelligence (AI) paradigm. In the proposed framework, wired and wireless signals are used to train a large teacher network and a small student network, respectively, enabling the student to learn channel-robust RFF representations. Specifically, we first develop the DrffNet-ResNet, which is based on the ResNet backbone and the spatial attention mechanism. The complexity of the proposed DrffNet-ResNet is analyzed, where the student network achieves a 27% reduction in parameters, but has the similar performance as the teacher network. Next, to prove the model-agnostic of the proposed framework, we also develop the DrffNet-MobileNet and evaluate the performance via the experiments. The experiment results demonstrate that the developed DrffNet-ResNet and DrffNet-MobileNet achieve an accuracy of above 95% and 93% on Long Range (LoRa) radio frequency (RF) datasets, respectively, under different channel effects and various signal-to-noise ratios (SNRs), and also achieve an outstanding performance on uncrewed aerial vehicle (UAV) RF datasets. The developed networks have significant advantages over the state-of-the-art deep learning (DL)-based RFFI methods in terms of accuracy, computation amount and universal applicability. Extensive system-level evaluations verify that the proposed cloud–edge collaborative RFFI framework enables efficient deployment, low-latency edge inference, and stable performance under continuous model updates.