预印本：在瑞利衰落条件下使用RF-DNA指纹对OFDM发射机进行分类
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
摘要：
物联网（IoT）是能够与物理世界和计算机系统交互的互联网连接设备的集合。据估计，到2020年，物联网将由大约500亿台设备组成。除了纯粹的数字之外，对物联网安全性的需求还因为许多边缘设备对通信链路的加密很弱甚至没有加密而加剧。据估计，近70%的物联网设备没有使用任何形式的加密。先前的研究已经建议使用特定的EQUIPMENT标识（SEI），一种物理层技术，作为增强比特级安全机制（例如加密）的手段。这里提出的工作集成了Nelder-Mead为基础的方法，用于估计瑞利衰落信道系数之前的SEI方法称为RF-DNA指纹。该估计器的性能进行评估，降低信号-噪声比，并与最小二乘和最小均方误差信道估计。此外，这项工作提出了分类结果，使用RF-DNA指纹，从接收到的信号，经历了瑞利衰落信道校正使用最小均方误差（MMSE）均衡。这项工作还进行了无线电歧视使用RF-DNA指纹生成的归一化幅度平方和相位响应的Gabor系数以及两个分类器。在信噪比为18 dB和21 dB或更高的情况下，分别使用由两条路径和五条路径组成的瑞利衰落信道，对四个802.11a Wi-Fi无线电进行区分时，平均正确分类百分比达到90%或更高。
英文摘要：
The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
