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Adam Wunderlich (Fed)

Group Leader, Applied Systems Metrology

Adam Wunderlich leads the Applied Systems Metrology Group in the Spectrum Technology and Research Division of the Communications Technology Laboratory.  His research integrates applied statistics, signal processing, and machine learning methods to characterize the behavior and performance of communication systems operating within congested, dynamic spectrum environments.  Dr. Wunderlich has coauthored over 70 technical publications.  

Awards

Allen V. Astin Measurement Science Award, NIST, 2020

Bronze Medal, U.S. Department of Commerce, 2020, 2022

Gold Medal, U. S. Department of Commerce, 2017

Selected Publications

Characterizing LTE User Equipment Emissions Under Closed-Loop Power Control

Author(s)
Jason Coder, Aric Sanders, Michael R. Frey, Adam Wunderlich, Azizollah Kord, Jolene Splett, Lucas N. Koepke, Daniel Kuester, Duncan McGillivray, John M. Ladbury
This report presents a laboratory-based characterization of Long Term Evolution (LTE) User Equipment (UE) emissions under closed-loop power control, building on...

AWS-3 LTE Impacts on Aeronautical Mobile Telemetry

Author(s)
William F. Young, Duncan McGillivray, Adam Wunderlich, Mark Krangle, Jack Sklar, Aric Sanders, Keith Forsyth, Mark A. Lofquist, Dan Kuester
This technical report details an effort to design, demonstrate, and validate a test methodology to measure the impacts of Long Term Evolution (LTE) User...

Characterizing LTE User Equipment Emissions: Factor Screening

Author(s)
Jason Coder, Adam Wunderlich, Michael R. Frey, Paul T. Blanchard, Dan Kuester, Azizollah Kord, Max Lees, Aric Sanders, Jolene Splett, Lucas N. Koepke, Rob Horansky, Duncan McGillivray, John M. Ladbury, Jeffrey T. Correia, Venkatesh Ramaswamy, Jerediah Fevold, Shawn Lefebre, Jacob K. Johnson, John Carpenter, Mark Lofquist, Keith Hartley, Melissa Midzor
Characterizations of long-term evolution (LTE) user equipment (UE) emissions are a key ingredient in models of interference between wireless cellular networks...

Selected Data and Software Publications

Noise Datasets for Evaluating Deep Generative Models

Author(s)
Adam Wunderlich, Jack Sklar
Synthetic training and test datasets for experiments on deep generative modeling of noise time series. Consists of data for the following noise types: 1) band-limited thermal noise, i.e., bandpass
Created October 9, 2019, Updated June 10, 2026
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