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 …

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

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

Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle
perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) …

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

With the emergence of the information age, mobile data has become more random,
heterogeneous and massive. Thanks to its many advantages, deep learning is increasingly …

Modulation recognition models based on deep neural network (DNN) have the advantages
of automatic feature extraction, fast recognition and high accuracy. However, due to the …

Deep learning (DL) is a powerful technique for many real-time applications, but it is
vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification …

Automatic Modulation Classification (AMC)-oriented Deep Neural Networks (ADNNs) have
received much attention in recent years for their wide range of applications. However, they …

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