Adversarial examples (AdExs) in machine learning for classification of radio frequency (RF)
signals can be created in a targeted manner such that they go beyond general …

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 learning techniques can classify spectrum phenomena (eg, waveform modulation)
with accuracy levels that were once thought impossible. Although we have recently seen …

Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …

Automatic modulation classification (AMC) is used in intelligent receivers operating in
shared spectrum environments to classify the modulation constellation of radio frequency …

While research on adversarial examples in machine learning for images has been prolific,
similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation …

Deep learning algorithms have been shown to be powerful in many communication network
design problems, including that in automatic modulation classification. However, they are …

Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …

While research on adversarial examples (AdExs) in machine learning for images has been
prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …