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 IoT will consist of more than seventy five billion devices by the year 2025. 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 mechanisms such as encryption. Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting is an SEI technique that has demonstrated success in discriminating radios operating within a noise only channel. This work extends RF-DNA fingerprinting to the discrimination of radios operating under Rayleigh fading conditions through the use of a Nelder-Mead (N-M) simplex-based channel estimator. The N-M estimator estimates the multipath channel directly from the received waveform; thus, eliminating the need for demodulation that is required when using constellation-based estimators. N-M estimator proves superior to three alternative waveform-based estimation approaches under increasing fading paths/reflections and decreasing Signal-to-Noise Ratio (SNR). Radio discrimination performance is maximized through the assessment of: (i) RF-DNA fingerprints generated from the magnitude versus phase representation of the Gabor transform’s coefficients, (ii) a statistic-based classifier versus a neural network-based classifier, and (iii) the size of patch used to subdivide the Gabor-based time-frequency response prior to calculation of the RF-DNA fingerprint features. The resulting RF-DNA fingerprinting process achieves an average percent correct classification of 92.3% or greater for Rayleigh fading channels consisting of: two, three, or five reflections/paths at SNR≥15 dB.

中文翻译：

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瑞利衰落条件下OFDM发射机的RF-DNA指纹识别的Nelder-Mead单工信道估计

物联网（IoT）是能够与物理世界和计算机系统进行交互的Internet连接设备的集合。据估计，到2025年，物联网将包含超过750亿个设备。除了数量庞大之外，由于许多边缘设备采用弱加密或不加密通信的事实，加剧了对物联网安全性的需求。关联。据估计，几乎70％的IoT设备不使用任何形式的加密。先前的研究建议使用特定的发射器标识（SEI）（一种物理层技术）作为增强诸如加密之类的比特级安全机制的手段。射频不同的本机属性（RF-DNA）指纹识别是一种SEI技术，已证明在区分纯噪声信道中运行的无线电方面取得了成功。这项工作通过使用基于Nelder-Mead（NM）单工的信道估计器，将RF-DNA指纹技术扩展到了在瑞利衰落条件下工作的无线电的辨别能力。NM估计器直接从接收到的波形中估计多径信道；因此，消除了使用基于星座图的估计器时所需的解调需求。在增加衰落路径/反射和降低信噪比（SNR）的情况下，NM估计器被证明优于三种基于波形的估计方法。通过评估（i）从Gabor变换系数的幅度相对于相位的表示中生成的RF-DNA指纹，（ii）基于统计的分类器与基于神经网络的分类器，可以最大程度地提高无线电识别性能 （iii）在计算RF-DNA指纹特征之前，用于细分基于Gabor的时频响应的贴片大小。对于包含以下内容的瑞利衰落信道，所得的RF-DNA指纹识别过程可实现92.3％或更高的平均正确分类百分比，该信道包括：SNR≥15dB的两个，三个或五个反射/路径。