Deep learning-based automatic modulation classification (AMC) models are susceptible to
adversarial attacks. Such attacks inject specifically crafted wireless interference into …

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

Deep learning is increasingly being used for many tasks in wireless communications, such
as modulation classification. However, it has been shown to be vulnerable to adversarial …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

Motivated by the superior performance of deep learning in many applications including
computer vision and natural language processing, several recent studies have focused on …

Although Deep Neural Networks (DNN) can achieve state-of-the-art performance in
automatic modulation recognition (AMC) tasks, they have sufferd tremendous failures from …

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

Deep neural networks provide intelligent solutions for Automatic Modulation Classification
(AMC) tasks in the field of communication. However, their susceptibility to adversarial …

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

With the improvement of basic designs and the evolution of key algorithms, artificial
intelligence (AI) has been considered by both industry and academia as the most promising …