动态干扰下RFID耦合认证的风险调控学习框架

吴海锋; 余文波; 曾玉; 杨江峰

doi:10.11999/JEIT251108

动态干扰下RFID耦合认证的风险调控学习框架

doi: 10.11999/JEIT251108 cstr: 32379.14.JEIT251108

吴海锋^{1, 2, 3, ,},
余文波¹,
曾玉^{1, 3},
杨江峰¹

1.
云南民族大学电气信息工程学院昆明 650504
2.
云南省无人自主系统重点实验室昆明 650504
3.
云南省高校智能传感网络及信息系统科技创新团队昆明 650504

基金项目: 国家自然科学基金(62161052)项目资助

详细信息

作者简介:
吴海锋：男，教授，研究方向为信号处理、机器学习

余文波：男，硕士生，研究方向为无线射频识别

曾玉：女，讲师，研究方向为电子信息、通信系统

杨江峰：男，讲师，研究方向为机器学习

通讯作者:
吴海锋　whf5469@gmail.com

中图分类号: TN911
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出版历程
- 修回日期: 2026-03-16
- 录用日期: 2026-03-16
- 网络出版日期: 2026-04-11

A Risk-Modulated Learning Framework for PHY RFID Authentication

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

摘要

摘要: 动态干扰环境下，射频识别(RFID)耦合认证的物理层特征易受金属反射、多径效应影响，导致传统静态建模方法识别稳定性不足。针对此问题，本文提出风险调控学习识别框架(RMLIF)，构建“风险感知—物理调节—特征重构—分类判定”的闭环机制。该框架创新性在于：(1)建立随机微分方程(SDE)信道模型，通过漂移项、扩散项与冲击项协同刻画动态干扰，证明解存在唯一性定理；(2)设计目标导向自适应风险(TDAR)调节算法，理论上保证风险指数单调收敛与扰动稳定性，等效实现分类边界间隔放大；(3)提出识别风险指数(RRI)与信干噪比(SINR)的指数映射关系，构建低维压缩特征空间，并推导出泛化误差界与样本复杂度界。基于通用软件无线电外设(USRP) N2000平台的实验表明，在无/小/中/大铜片干扰场景下，RMLIF识别准确率均达90%以上，较传统方法平均提升10%-20%，验证了理论分析的正确性与工程应用价值。
- RFID /
- 防伪 /
- 耦合 /
- 识别风险 /
- 分类
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, leading to unstable recognition. To address this issue, this paper proposes a Risk-Modulated Learning Identification Framework (RMLIF) that integrates stochastic channel modeling, adaptive risk regulation, and risk-regularized classification. The objective is to achieve stable and interpretable physical-layer authentication under nonstationary interference, thereby improving the anti-counterfeiting reliability of RFID systems. Methods An SDE-based coupled channel model is first established to jointly describe drift, diffusion, and impulsive interference (Eq.(1)), and its solution’s existence and uniqueness are proved. A Target-Driven Adaptive Risk (TDAR) algorithm dynamically adjusts 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 SINR is analytically characterized (Eq.(11), Fig.3). This enables real-time risk estimation and closed-loop control. For feature representation, a difference-based compressive modeling method captures the perturbation between normalized and reference signals (Fig.1), and Theorem 1 ensures Lipschitz stability. Parallel statistical and steady-state feature paths are designed (Table 3), and their joint robustness is proved (Corollary 4). The framework further establishes that TDAR regulation is equivalent to a risk regularization process (Theorem 3), effectively enlarging 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 enhances efficiency. The Asymptotic Real Risk Index (ARRI) (Theorem 8) explains long-term convergence and system self-consistency. Experiments on a USRP N2000 platform (Table 5) using six types of EPC Gen2 tags under four interference levels—no, small, medium, and large copper plates (Fig.4)—validate the proposed framework. Compared with conventional methods (Coupling_14, Hu_Fu, CNN_Vgg19, PCFM), their RMLIF-enhanced versions exhibit significant accuracy gains (Fig.5). The PCFM_RMLIF achieves over 90% accuracy, improving by 15%–28%. PCA visualization confirms the stability of compressed features (Fig.6) and clearer class separation after risk regulation (Fig.7). The TDAR algorithm converges rapidly within two iterations (Fig.9). As the effective sample ratio and feature dimension increase, RRI decreases monotonically (Fig.10), aligning with Theorem 6. Entropy analysis (Fig.11) indicates lower system uncertainty and improved stability after regulation. Cross-condition tests further verify the framework’s robustness and generalization (Fig.12). Conclusions This paper develops a unified stochastic–adaptive learning framework for 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 linking physical signals to cognitive risk. Both theory and experiments confirm that risk-driven modulation effectively suppresses disturbance, enhances feature separability, and reduces generalization error. The proposed approach achieves high accuracy, fast convergence, and strong robustness, offering a general solution for dynamic RFID anti-counterfeiting. Future work will extend this framework to multi-tag cooperative scenarios and low-power cross-band authentication.
- RFID /
- Anti-counterfeiting /
- Coupling /
- Recognition Risk /
- Classification

HTML全文

图 1 原始标签响应信号与预处理信号

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图 2 风险调控学习识别框架

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图 3 SINR与识别风险指数(RRI)的映射关系

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图 4 标签、天线和铜片位置摆放图

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图 5 各算法在各干扰场景下分类准确率图

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图 6 第5类标签(其余类标签情况相似)的特征性能图

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图 7 风险调控前后的组合特征PCA降维图，其中A为第二类标签，B为其他类标签

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图 8 平均6类标签的风险调节机制的第一阶段“先验知识”图

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图 9 第5类标签的风险调节收敛步数

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图 10 RRI 随特征数目、调控比例和样本占比的变化

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图 11 风险调控对香农熵与统计稳定性的影响

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图 12 平均6类标签从训练迁移至测试条件的风险指数结果图

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表 4 RMLIF算法

算法3：RMLIF
输入：读写器接收信号 $ \mathbf{x} $
输出：分类标签 $ \widehat{y} $
步骤：
(1) 参数调节：由算法2进行参数调节得到小风险参数 $ {\mathbf{\theta }}^{*} $
(2) 特征值：由表3的特征映射 $ \phi (\cdot ) $ 计算特征
(3) 分类：将步骤(2)中的特征值输入至分类器得到分类标签 $ \widehat{y} $

下载: 导出CSV

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