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Aric Sanders (Assoc)

Publications

Laboratory Method for Recording AWS-3 LTE Waveforms

Author(s)
Aric Sanders, Keith Forsyth, Rob Horansky, Azizollah Kord, Duncan McGillivray
The focus of this work provides a library of actual long-term evolution (LTE) user equipment (UE) emissions with sufficient time resolution and dynamic range

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

Machine Learning in a Quality Managed RF Measurement Workflow

Author(s)
Aric W. Sanders, John Bass, Arpita Bhutani, Mary A. Ho, James C. Booth
Advances in artificial intelligence, or more specifically machine learning, have made it possible for computers to recognize patterns as well or better than
Created June 7, 2019, Updated June 15, 2021