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Search Publications by: Jacob Rezac (Fed)

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Displaying 1 - 23 of 23

On Digital Signal Processing of Time Series for Spectrum Estimation

September 11, 2024
Author(s)
Dazhen Gu, Jake Rezac, Xifeng Lu, Dan Kuester
We present a study of power spectral density (PSD) estimation from data sampled in the time domain. This work was motivated by our recent development of digital radiometry, where radiation spectra were obtained by processing the digitally sampled signal

Robust Measurements for RF Fingerprinting with Constellation Patterns of Radiated Waveforms

November 20, 2023
Author(s)
Ameya Ramadurgakar, Jake Rezac, Lennart Heijnen, Kate Remley, Dylan Williams, MELINDA PIKET-MAY, Rob Horansky
We introduce a type of RF fingerprint for nondestructive, cellular device identification. The new fingerprinting algorithm, termed Eigenphones, is a data-driven technique based on a singular value decomposition of a user equipment's symbol-constellation

A Measurement-Referenced Error Vector Magnitude for Counterfeit Cellular Device Detection

October 17, 2023
Author(s)
Ameya Ramadurgakar, Kate Remley, Dylan Williams, Jake Rezac, MELINDA PIKET-MAY, Rob Horansky
Standard formulations of error vector magnitude compare a wireless device's symbol constellation to an ideal reference constellation. In this work, we utilize the residual error vector magnitude, which uses measurements of a wireless device to define a

A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks

July 6, 2023
Author(s)
Jason Killgore, Thomas Kolibaba, Benjamin Caplins, Callie Higgins, Jake Rezac
Machine learning models such as U-nets like the pix2pix conditional generative adversarial network (cGAN) are shown to predict 3D printed voxel geometry in digital light processing (DLP) additive manufacturing. The models are trained on microscopic voxel

Radio Spectrum Occupancy Measurements Amid COVID-19 Telework and Telehealth

October 14, 2022
Author(s)
Dan Kuester, Xifeng Lu, Dazhen Gu, Azizollah Kord, Jake Rezac, Katie Carson, Marla L. Dowell, Elizabeth Eyeson, Ari Feldman, Keith Forsyth, Vu Le, John Marts, Mike McNulty, Kyle Neubarth, Andre Rosete, Matthew Ryan, Maija Teraslina
During the COVID-19 pandemic, NIST began a targeted campaign of measurements of averaged power and occupancy rate in the radio spectrum. The purpose was to sample real environments to offer timely insights into data infrastructure where it might be

Telecommunication Testbed Repeatability Assessment

September 16, 2022
Author(s)
Jeanne Quimby, Jake Rezac, Mary Gregg, Michael Frey, Jason Coder, Anna Otterstetter
Telecommunication testbeds are a fundamental tool in communication research, enabling prototyping and validating new ideas. Unfortunately, these testbeds are often highly complex, costly, and challenging to operate, requiring simultaneous Open System

Bi-Criteria Radio Spectrum Sharing with Subspace-Based Pareto Tracing

March 22, 2022
Author(s)
Zachary J. Grey, Susanna Mosleh, Jake Rezac, Yao Ma, Jason Coder, Andrew Dienstfrey
Radio spectrum is a scarce resource. To meet demands, new wireless technologies must operate in shared spectrum over unlicensed bands (coexist). We consider coexistence of Long-Term Evolution (LTE) License-Assisted Access (LAA) with incumbent Wi-Fi systems

Monte Carlo Augmented Channel Estimator

March 3, 2022
Author(s)
Alec Weiss, Atef Elsherbeni, Jeanne Quimby, Jake Rezac
We have developed a novel Monte Carlo-augmented channel estimator for millimeter-wave orthogonal frequency division multiplexing systems To better estimate and correct a transmitted orthogonal frequency division multiplexed signal by incorporating the

Estimating Regions of Wireless Coexistence with Gaussian Process Surrogate Models

October 22, 2021
Author(s)
Jake Rezac, Noel C. Hess, Jason Coder
Simultaneous coexistence of multiple wireless communications systems sharing the same spectrum is critical for the success of modern and future communications. We develop a technique for estimating regions of wireless coexistence (RWC) – the transmission

Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing

August 6, 2021
Author(s)
Zachary J. Grey, Susanna Mosleh, Jake Rezac, Yao Ma, Jason Coder, Andrew Dienstfrey
In order to meet the ever-growing demands of data throughput for forthcoming and deployed wireless networks, new wireless technologies like Long-Term Evolution License-Assisted Access (LTE-LAA) operate in shared and unlicensed bands. However, the LAA

Timing Offset and Timing Stability for Dual-Clock Systems

May 4, 2021
Author(s)
Joshua Kast, Jeanne Quimby, Jake Rezac, Stefania Romisch
In this work, we describe a mathematical framework for evaluating timing offset and timing noise in channel sounders, based on a second-order deterministic model, and a stochastic metric based on the Allan Deviation. Using this framework, we analyze the

Evaluating Uncertainty of Microwave Calibrations with Regression Residuals

June 6, 2020
Author(s)
Dylan F. Williams, Benjamin F. Jamroz, Jake D. Rezac, Robert D. Jones
We present a sensitivity-analysis and a Monte-Carlo algorithm for evaluating the uncertainty of multivariate microwave calibration models with regression residuals. We then use synthetic data to verify the performance of the algorithms and explore their

Channel Sounder Measurement Verification: Conducted Tests

April 15, 2020
Author(s)
Jeanne T. Quimby, Jeffrey A. Jargon, Rodney W. Leonhardt, Jake D. Rezac, Paul D. Hale, Catherine A. Remley, Amanda A. Koepke, Robert Johnk, chriss Hammerschmidt, Paul Mckenna, Irena Stange, Mike Chang
Channel modeling often provides a basis for the design and deployment of wireless technology. Engineers design systems to operate under certain expected channel conditions. Channel models are typically based on the statistics of a collection of many

Monte Carlo Sampling Bias in the Microwave Uncertainty Framework

June 27, 2019
Author(s)
Michael R. Frey, Benjamin Jamroz, Amanda Koepke, Jake Rezac, Dylan Williams
The Microwave Uncertainty Framework (MUF) is a software suite created, supported, and made publicly available by the Radio Frequency Division of the U.S. National Institute of Standards and Technology. The general purpose of the MUF is to provide automated

Large-Signal Network Analysis for Over-the-Air Test of Up-Converting and Down-Converting Phased Arrays

June 1, 2019
Author(s)
Alec Weiss, Dylan Williams, Jeanne Quimby, Rod Leonhardt, Thomas Choi, Zihang Chen, Kate Remley, andreas Molisch, Ben Jamroz, Jake Rezac, Peter Vouras
We explore large-signal network analysis for the over-the-air test of up-converting and down-converting phased arrays. The approach first uses a vector network analyzer to calibrate the impulse response of an over-the-air test system at RF. The vector

A Systematic Study: Channel Sounding via Modal Expansion

November 3, 2018
Author(s)
Alex Yuffa, Ben Jamroz, Jake Rezac, Dylan Williams
We present preliminary results of using a modal (partial wave) expansion of the field to characterize a propagation channel. We assume that the measurements of the scalar, two-dimensional field from which the modal expansion coefficients are obtained