This dataset contains results represented in the work titled 'A Robust, Over-the-Air Test Bed for Radio-Frequency Fingerprinting of Cellular Devices', whose abstract sample is below. We present a characterized test bed and algorithms for non-destructive, over-the-air fingerprinting of commercial cellular user equipment (UE). This test bed is designed to repeatably collect radiated fields from cellular devices in a 4G long term evolution (LTE) network configuration. We describe a straightforward classification algorithm to determine the model of each cellular device that allows for a direct correlation between input data from test cellular phones and identification efficacy. Additionally, by controlling the radio channel conditions, we provide a framework for transparently studying dominant uncertainties and sensitivities in data-driven cellular device fingerprinting. The algorithm performs classification with either the error vector magnitude, a quantity derived from demodulated data, or the out-of-band frequency domain response of the cellular devices. We have investigated the robustness over time of this fingerprinting method and show over 95% accuracy in identifying UE models from different manufacturers and gaining insight into parameters that can cause a reduction in this level of accuracy and in data-driven approaches in general. This work is part of a larger effort to identify and create a database of genuine off-the-shelf cellular devices to help mitigate counterfeiting and hardware security tampering using RF fingerprinting. As such, the raw data are text files in comma separated value (CSV) format. The text files have varying numbers of columns depending on the figure it is attributed to.

This data is associated with the manuscript titled Radio-Frequency Fingerprinting Millimeter-Wave Commercial Off-the-Shelf Cellular User Equipment. The abstract of this manuscript is: We present a measurement test bed and algorithm for non-destructive, millimeter wave over-the-air fingerprinting of commercial off-the-shelf cellular user equipment (UE) at the model level. This test bed has been configured to repeatably collect radiated fields from UEs in a 5G mmWave scenario. We evaluate the performance of a classification algorithm that uses linear discriminant analysis on two types of measured waveforms from cellular user equipment under test. One of the waveforms is the user equipment transmitted symbol constellation vector, which is complex-valued. The classification algorithm uses the symbol constellation waveforms collected from the UEs to generate a unique fingerprint of the test UE. The other waveform is the filtered uplink spectrum waveform from the UEs. We evaluate the performance of our test bed and algorithm by analyzing the temporal stability of the data sets for the purposes of fingerprinting and quantifying the classification performance of the test bed and algorithm.

This data is associated with the manuscript titled Radio-Frequency Fingerprinting Millimeter-Wave Commercial Off-the-Shelf Cellular User Equipment. The abstract of this manuscript is: We present a measurement test bed and algorithm for non-destructive, millimeter wave over-the-air fingerprinting of commercial off-the-shelf cellular user equipment (UE) at the model level. This test bed has been configured to repeatably collect radiated fields from UEs in a 5G mmWave scenario. We evaluate the performance of a classification algorithm that uses linear discriminant analysis on two types of measured waveforms from cellular user equipment under test. One of the waveforms is the user equipment transmitted symbol constellation vector, which is complex-valued. The classification algorithm uses the symbol constellation waveforms collected from the UEs to generate a unique fingerprint of the test UE. The other waveform is the filtered uplink spectrum waveform from the UEs. We evaluate the performance of our test bed and algorithm by analyzing the temporal stability of the data sets for the purposes of fingerprinting and quantifying the classification performance of the test bed and algorithm.

The raw data from this experiment are recorded voltage waveforms versus time from a calibrated equivalent time sampling oscilloscope, touchstone files from network analysis, and waveforms in both frequency and time domain from a vector receiver. As such, the raw data are text files in MATLAB format.The text files recorded from the oscilloscope end with .mat and can have varying numbers of columns depending on the number of scope channels that were recorded.

Raw data from this project are recorded voltage waveforms versus time from a calibrated real time oscilloscope, waveforms in both frequency, time and modulated domain in both frequency and time domain from a vector receiver. The raw data are text files in MATLAB format. The text files recorded from the oscilloscope end with .mat and can have varying number of columns depending on the number of scope channels that were recorded.

Raw data from this project are recorded voltage waveforms versus time from a calibrated real time oscilloscope, waveforms in both frequency, time and modulated domain in both frequency and time domain from a vector receiver. The raw data are text files in MATLAB format. The text files recorded from the oscilloscope end with .mat and can have varying number of columns depending on the number of scope channels that were recorded.

This project [1] produced two types of data: Working Data and Derived Data. The Working Data includes raw measurements collected from a Radio Access Network (RAN) utilizing commercial-off-the-shelf (COTS) hardware. These measurements were gathered at multiple layers of the Long-Term Evolution (LTE) protocol stack within the RAN, in both a baseline state with encrypted data and an anomalous state with encryption disabled. The data is provided in comma-separated value (.csv) format.The Working Data was then processed into Derived Data using Python 3 scripts that performed statistical analyses to explore data distributions. The Derived Data is also available in .csv files. Additionally, human-readable spreadsheets are included, offering measurement notes and definitions related to the Working Data. Please note that the data set is divided into three distinct tranches, or sections, in alignment with the project's design of experiment.[1] M. Frey, T. Evans, A. Folz, M. Gregg, J. Quimby, J. Rezac. ?Anomalous state detection in radio access networks: A proof-of-concept.? Journal of Network and Computer Applications. Volume 231, 2024, https://doi.org/10.1016/j.jnca.2024.103979.

The data correspond to the paper Practical Correlation-Matrix Approaches for Standardized Testing of Wireless Devices in Reverberation Chambers. Abstract: We extend the autocorrelation-based approaches currently used in standards to full correlation matrix-based approaches in order to identify correlation between both spatially adjacent and non-adjacent samples in reverberation-chamber measurements. We employ a scalar metric that allows users to identify the number of effectively uncorrelated samples in new types of stirring sequences. To make these approaches practical and enhance their accuracy, we implement a thresholding technique that retains correlation related to important aspects of chamber configuration such as loading and undermoded conditions. We develop a method to propagate uncertainty in the complex correlation coefficients through to the number of effective samples for a given reverberation-chamber set-up by use of a bootstrap technique that is accurate even for highly skewed distributions of correlation coefficients. We further apply this method in a sensitivity studyregarding the choice threshold value. Agreement with existing approaches in determining the number of effectively uncorrelated samples is presented for a measurement example where spatially adjacent samples are utilized. Examples are then illustrated for non-spatially-adjacent correlated samples at microwave and millimeter-wave frequencies.

This repository contains code for analyzing industrial wireless sounder measurements and the generation of wireless scenarios. DISCLAIMER: Certain commercial equipment, instruments, or materials are identified in the associated paper (https://doi.org/10.6028/nist.tn.1951) in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

This repository contains code for analyzing industrial wireless sounder measurements and the generation of wireless scenarios. DISCLAIMER: Certain commercial equipment, instruments, or materials are identified in the associated paper (https://doi.org/10.6028/nist.tn.1951) in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.