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

Future wireless communications are becoming increasingly complex with different radio
access technologies, transmission backhauls, and network slices, and they play an …

Security is one of the biggest challenges concerning networks and communications. The
problem becomes aggravated with the proliferation of wireless devices. Artificial Intelligence …

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

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

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 …

In this paper, reinforcement learning (RL) for network slicing is considered in next
generation (NextG) radio access networks, where the base station (gNodeB) allocates …

Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt
cooperative spectrum sensing by sending falsified versions of spectrum sensing data to a …

Network slicing manages network resources as virtual resource blocks (RBs) for the 5G
Radio Access Network (RAN). Each communication request comes with quality of …

We consider adversarial machine learning based attacks on power allocation where the
base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a …