Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

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

Although semantic communications have exhibited satisfactory performance on a large
number of tasks, the impact of semantic noise and the robustness of the systems have not …

Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

The spoofing attack is critical to bypass physical-layer signal authentication. This paper
presents a deep learning-based spoofing attack to generate synthetic wireless signals that …

Communications systems to date are primarily designed with the goal of reliable transfer of
digital sequences (bits). Next generation (NextG) communication systems are beginning to …

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for
important tasks in major wireless communication systems: channel estimation and decoding …

Machine learning provides automated means to capture complex dynamics of wireless
spectrum and support better understanding of spectrum resources and their efficient …

Semantic communication seeks to transfer information from a source while conveying a
desired meaning to its destination. We model the transmitter-receiver functionalities as an …

As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has
become imperative to understand the vulnerabilities of the IoT systems when supporting …