1 code implementation • 20 Dec 2023 • Daniel Rosen, Illa Rochez, Caleb McIrvin, Joshua Lee, Kevin D'Alessandro, Max Wiecek, Nhan Hoang, Ramzy Saffarini, Sam Philips, Vanessa Jones, Will Ivey, Zavier Harris-Smart, Zavion Harris-Smart, Zayden Chin, Amos Johnson, Alyse M. Jones, William C. Headley
Index Terms-machine learning, reinforcement learning, wireless communications, dynamic spectrum access, OpenAI gym
no code implementations • 18 Oct 2022 • Anders M. Buvarp, Daniel J. Jakubisin, William C. Headley, Jeffrey H. Reed
In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G communication system.
no code implementations • 9 Jul 2021 • Richard Lau, Lihan Yao, Todd Huster, William Johnson, Stephen Arleth, Justin Wong, Devin Ridge, Michael Fletcher, William C. Headley
We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity.
no code implementations • 13 Oct 2020 • Megan Moore, William H. Clark IV, R. Michael Buehrer, William C. Headley
Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals.
no code implementations • 1 Oct 2020 • Lauren J. Wong, William H. Clark IV, Bryse Flowers, R. Michael Buehrer, Alan J. Michaels, William C. Headley
While deep machine learning technologies are now pervasive in state-of-the-art image recognition and natural language processing applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications.
no code implementations • 1 Oct 2020 • William H. Clark IV, Steven Hauser, William C. Headley, Alan J. Michaels
Looking into the Radio Frequency Machine Learning (RFML) field of Automatic Modulation Classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels.
no code implementations • 27 May 2020 • Matthew DelVecchio, Vanessa Arndorfer, William C. Headley
However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal.
no code implementations • 27 May 2020 • Matthew DelVecchio, Bryse Flowers, William C. Headley
Recent work has shown the impact of adversarial machine learning on deep neural networks (DNNs) developed for Radio Frequency Machine Learning (RFML) applications.
no code implementations • 25 Sep 2019 • Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu, William C. Headley, Michael Fowler, Gilbert Green
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network.
no code implementations • 1 Mar 2019 • Bryse Flowers, R. Michael Buehrer, William C. Headley
Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks.