Physical layer security is increasingly being exploited as a technique to enhance the security of wireless communications. Well-known hardware security techniques leverage unintended manufacturing process variations or fixed unique hardware structures in the semiconductors for identification of different copies of an RF system. The fundamentally different concept of engineering a unique fingerprint for each antenna produced, by leveraging additive manufacturing, is presented in this work. To the best of the author’s knowledge, this is the first application of the concept of radio frequency (RF) fingerprint engineering using additively manufactured (AM) antennas for hardware-based security mechanisms. AM engineered fingerprints (AMEF) are based on intentional features added to antenna’s geometries using 3D printing, enabling more accurate signal source identification and classification. Such unique features per unit produced would be prohibitively costly by using traditional photolithographic processes. The AMEF concept is validated using AM right hand circularly polarized (RHCP) truncated corner probe fed (TCPF) patch antennas and a testbed setup with software-defined radios and MATLAB implementations to create, transmit, receive, and prepare a convolutional neural network (CNN) to classify the transmitted raw I/Q data of Wi-Fi signals (IEEE 802.11). The AMEF technique greatly improves the physical layer security performance limitations in large-scale applications where the features of the devices can overlap, at a low-cost and low production time impact, and it allows the use of the antenna features for both individual and type classification and identification of RF sources.

中文翻译：

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增材制造天线工程指纹识别可增强无线通信的物理层安全性


物理层安全性越来越多地被用作增强无线通信安全性的技术。众所周知的硬件安全技术利用半导体中意外的制造工艺变化或固定的独特硬件结构来识别射频系统的不同副本。这项工作提出了利用增材制造为每个生产的天线设计独特指纹的根本不同概念。据作者所知，这是射频（RF）指纹工程概念的首次应用，使用增材制造（AM）天线来实现基于硬件的安全机制。 AM 工程指纹 (AMEF) 基于使用 3D 打印添加到天线几何形状的有意特征，从而实现更准确的信号源识别和分类。如果使用传统的光刻工艺，每单位生产的这种独特特征的成本将高得令人望而却步。 AMEF 概念使用 AM 右旋圆极化 (RHCP) 截角探头馈电 (TCPF) 贴片天线以及具有软件定义无线电和 MATLAB 实现的测试台设置进行验证，以创建、传输、接收和准备卷积神经网络 (CNN) ）对传输的 Wi-Fi 信号原始 I/Q 数据进行分类 (IEEE 802.11)。 AMEF技术以低成本和低生产时间影响极大地改善了大规模应用中设备功能可能重叠的物理层安全性能限制，并且允许使用单独和类型的天线功能射频源的分类和识别。