发表机构
Ben-Gurion University of the Negev; Elbit Systems（内盖夫本-古里安大学; 埃尔比特系统公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于学习辅助量化的整体射频指纹识别框架，在单一架构中联合支持闭集识别、开放集识别和新类别发现，实验验证其准确性与有效性。
AI 中文摘要
射频指纹识别（RFFI）通过利用接收到的射频信号中嵌入的发射器特定硬件损伤来实现设备识别和认证。现有的RFFI方法通常针对特定运行场景设计，如闭集识别、开放集识别（OSR）或新设备发现，并且往往将这些任务视为独立的算法组件。在本文中，我们提出了一种整体RFFI框架，在单一可扩展架构内联合支持闭集识别、OSR和新类别发现（NCD）。所提出的方法将RFFI形式化为在学习的潜在特征空间中的向量量化，其中已注册设备由带标签的码字表示。这种表示使得通过码字分配进行闭集识别、通过码本几何结构引起的不确定性度量进行OSR、以及通过利用被拒绝样本的潜在表示和开放集不确定性将其组织成新设备身份来进行NCD成为可能。我们进一步开发了一种多阶段训练流程，结合了监督表示学习、潜在空间细化、码本初始化和向量量化感知优化。在LoRa和WiFi RFFI数据集上的实验结果表明，所提出的框架能够准确识别已注册设备、可靠检测未见过的发射器，并有效发现和注册新设备类别，凸显了耦合不同RFFI任务的优势。
英文摘要
Radio frequency fingerprint identification (RFFI) enables device identification and authentication by exploiting transmitter-specific hardware impairments embedded in received RF signals. Existing RFFI methods are typically designed for a specific operating regime, such as closed-set identification, open-set recognition (OSR), or discovery of new devices, and often treat these tasks as separate algorithmic components. In this paper, we propose a holistic RFFI framework that jointly supports closed-set identification, OSR, and novel class discovery (NCD) within a single expandable architecture. The proposed method formulates RFFI as vector quantization in a learned latent feature space, where registered devices are represented by labeled codewords. This representation enables closed-set identification through codeword assignment, OSR through uncertainty measures induced by the codebook geometry, and NCD by using the latent representations and open-set uncertainty of rejected samples to organize them into new device identities. We further develop a multi-stage training procedure that combines supervised representation learning, latent-space refinement, codebook initialization, and vector-quantization-aware optimization. Experimental results on LoRa and WiFi RFFI datasets demonstrate that the proposed framework achieves accurate identification of registered devices, reliable detection of unseen transmitters, and effective discovery and registration of new device classes, highlighting the benefits of coupling the different RFFI tasks.
CommentsUnder review for publication in the IEEE
