A Risk-modulated Learning Framework for Physical-layer RFIDAuthentication under Dynamic Interference

WU Haifeng; YU Wenbo; ZENG Yu; YANG JiangFeng

doi:10.11999/JEIT251108

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WU Haifeng, YU Wenbo, ZENG Yu, YANG JiangFeng. A Risk-modulated Learning Framework for Physical-layer RFIDAuthentication under Dynamic Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251108

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WU Haifeng, YU Wenbo, ZENG Yu, YANG JiangFeng. A Risk-modulated Learning Framework for Physical-layer RFIDAuthentication under Dynamic Interference[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251108

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A Risk-modulated Learning Framework for Physical-layer RFIDAuthentication under Dynamic Interference

doi: 10.11999/JEIT251108 cstr: 32379.14.JEIT251108

WU Haifeng^{1, 2, 3
,
,},
YU Wenbo¹,
ZENG Yu^{1, 3},
YANG JiangFeng¹

1.
School of Electrical Information, Yunnan Minzu University, Kunming 650504, China
2.
Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China
3.
Intelligent Senor Network & Information System Innovative Research Team in Science and Technology in University of Yunnan Province, Kunming 650504, China

Funds: The National Natural Science Foundation of China (62161052)

Received Date: 2025-10-20
Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-12

Available Online: 2026-04-11

Abstract

Abstract

Objective Dynamic interference and metallic reflections severely affect the reliability of coupled Radio Frequency IDentification (RFID) authentication. Conventional static models cannot adapt to time-varying noise and multipath effects, which leads to unstable recognition. To address this problem, this paper proposes a Risk-Modulated Learning Identification Framework (RMLIF) that integrates stochastic channel modeling, adaptive risk regulation, and risk-regularized classification. The aim is to achieve stable and interpretable physical-layer authentication under nonstationary interference, thereby improving the anti-counterfeiting reliability of RFID systems. Methods A Stochastic Differential Equation (SDE)-based coupled channel model is first established to jointly characterize drift, diffusion, and impulsive interference (Eq.(1)), and the existence and uniqueness of its solution are proved. A Target-Driven Adaptive Risk (TDAR) algorithm is then designed to dynamically adjust physical-layer parameters based on the Recognition Risk Index (RRI). The RRI is derived from classification posterior probabilities (Eq.(3)), and its exponential mapping to the Signal-to-Interference-plus-Noise Ratio (SINR) is characterized analytically (Eq.(11), Fig. 3), which enables real-time risk estimation and closed-loop control. For feature representation, a difference-based compressive feature modeling method is used to capture the perturbation between normalized and reference signals (Fig. 1), and Theorem 1 establishes the stability of the compressed mapping. Parallel steady-state and perturbation feature paths are further designed (Table 3), and their joint robustness is proved in Corollary 4. In addition, the framework shows that TDAR regulation is equivalent to a risk-regularized classification process (Theorem 3), which effectively enlarges the classification margin without modifying the classifier structure. Results and Discussions Theoretical analysis derives the generalization error bound, sample complexity, and robustness limits (Theorem 4～7), showing that filtering high-risk samples reduces redundancy and improves learning efficiency. The Asymptotic Real Risk Index (ARRI) is further defined to explain long-term convergence and structural self-consistency (Theorem 8). Experiments conducted on a USRP N2000 platform (Table 5) use six types of EPC C1 Gen2 tags under four interference conditions, namely no copper plate and small, medium, and large copper plates (Fig. 4). Compared with conventional methods, including Coupling_14, Hu_Fu, CNN_Vgg19, and PCFM, the corresponding RMLIF-enhanced versions achieve clear gains in classification accuracy (Fig. 5). In all no/small/medium/large copper-plate interference scenarios, the proposed framework achieves accuracy above 90%, with an average improvement of 10%–20% over traditional methods. PCFM_RMLIF achieves the best overall performance. PCA visualization confirms the stability of the compressed features (Fig. 6) and the clearer class separation after risk regulation (Fig. 7). The TDAR algorithm converges rapidly, generally within two iterations (Fig. 9). As the effective sample ratio and feature dimension increase, the RRI decreases monotonically (Fig. 10), in agreement with Theorem 6. Entropy analysis (Fig. 11) shows that risk regulation reduces system uncertainty and improves stability. Cross-condition tests further verify the robustness and generalization ability of the framework (Fig. 12). Conclusions This paper develops a unified risk-modulated learning framework for physical-layer RFID authentication under dynamic interference. The RMLIF framework combines SDE-based channel modeling, adaptive TDAR regulation, and compressive feature reconstruction into a closed-loop mechanism that links physical signals with recognition risk. Both theoretical analysis and experimental results show that risk-driven regulation effectively suppresses disturbance, improves feature separability, and reduces generalization error. The proposed approach achieves high accuracy, rapid convergence, and strong robustness, and provides an effective solution for dynamic RFID anti-counterfeiting authentication.
- Radio Frequency IDentification(RFID),
- Anti-counterfeiting,
- Coupling,
- Recognition risk,
- Classification

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