Kernel-based learning (KBL) methods have recently become prevalent in many engineering
applications, notably in signal processing and communications. The increased interest is …

In this survey paper, we characterize the learning problem in cognitive radios (CRs) and
state the importance of artificial intelligence in achieving real cognitive communications …

Spectrum inference, also known as spectrum prediction in the literature, is a promising
technique of inferring the occupied/free state of radio spectrum from already …

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their
environment. To date, most cognitive radio research has focused on policy-based radios that …

Due to exponential growth in demand for radio spectrum for wireless communication
networking, the radio spectrum has become over-crowded. The fixed spectrum allocation …

The next frontier in wireless connectivity lies at the intersection of cognitive radio (CR)
technology and machine learning (ML), where intelligent networks can provide pervasive …

A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …

Intelligence is needed to keep up with the rapid evolution of wireless communications,
especially in terms of managing and allocating the scarce, radio spectrum in the highly …

We introduce an adaptive distributed technique that is suitable for parameter estimation in a
network where nodes have different but overlapping interests. At each node, the parameters …

Over the last decade the world of wireless communications has been undergoing some
crucial changes, which have brought it at the forefront of international research and …