The advent of sixth-generation (6G) networks is expected to start a new era in mobile
networks, characterized by unprecedented high demands on dense connectivity, ultra …

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

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

The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …

Signal classification is a universal problem in adversarial wireless scenarios, especially
when an eavesdropping radio receiver attempts to glean information about a target …

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

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

Due to great success of transformers in many applications, such as natural language
processing and computer vision, transformers have been successfully applied in automatic …

Recent work has advocated for the use of deep learning to perform power allocation in the
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …

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 …