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

Initially introduced almost thirty years ago for the express purpose of providing electronic
warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific …

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

Deep learning has great potential for automatic modulation classification (AMC). However,
its performance largely hinges upon the availability of sufficient high-quality labeled data. In …

Physical layer authentication relies on detecting unique imperfections in signals transmitted
by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators …

While deep learning (DL) technologies are now pervasive in state-of-the-art Computer
Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have …

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

While deep machine learning technologies are now pervasive in state-of-the-art image
recognition and natural language processing applications, only in recent years have these …

Fifth Generation (5G) networks must support billions of heterogeneous devices while
guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet …

Automatic modulation recognition (AMR) of radio signal is an important research topic in the
area of non-cooperative communication and cognitive radio. Recently deep learning (DL) …