38 citations · 63 across the 4 of their papers we have counts for
4 papers
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
Lauren J. Wong, William H. Clark, Bryse Flowers +3
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 techn…
Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features
J. B. Persons, Lauren J. Wong, W. Chris Headley +1
To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and…
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Yalin E. Sagduyu, Yi Shi, Tugba Erpek +4
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense s…
Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications
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. Yet, deep…