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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&#x2265;15 dB.

Nelder-Mead Simplex Channel Estimation for the RF-DNA Fingerprinting of OFDM Transmitters Under Rayleigh Fading Conditions