This paper presents a systematic and comprehensive survey that reviews the latest research
efforts focused on machine learning (ML) based performance improvement of wireless …

One of the key problems in spectrum sensing is to design the test statistic. Existing methods
generally exploit the model-based features as the test statistic, such as energies and …

Shared spectrum usage is inevitable due to the ongoing increase in wireless services and
bandwidth requirements. Spectrum monitoring is a key enabler for efficient spectrum sharing …

In this paper, we present a spectrum monitoring framework for the detection of radar signals
in spectrum sharing scenarios. The core of our framework is a deep Convolutional Neural …

This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-
operation awareness of radio communication nodes, links, and networks. The meaning and …

Transfer learning has allowed for more widespread adaptation and expanded use of deep
learning models in fields such as computer vision and speech recognition. The radio …

Due to the growing number of devices accessing the Internet through wireless networks, the
radio spectrum has become a highly contended resource. The availability of low cost radio …

Spectrum sensing (SS) is a concept of cognitive radio systems at base transceiver stations
that can find the white space ie licensed spectrum owned by primary users (PU), for …

Radio transmitters can be identified by their unique radio frequency (RF) fingerprints.
Hardware differences and manufacturing variations between different RF devices impart the …

Spectrum sensing is the most crucial importance in cognitive radios. We propose a novel
machine-learning algorithm for spectrum sensing in cognitive radio networks, which plays …