In this paper, we present a survey on the utility of machine learning (ML) algorithms for
applications in cognitive radio networks (CRN). We start with a high-level overview of some …

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 …

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

Deep reinforcement learning has recorded remarkable performance in diverse application
areas of artificial intelligence: pattern recognition, robotics, object segmentation …

A cognitive radio (CR) network consists of wireless devices that opportunistically borrow
vacant licensed bands. Cognitive users adaptively employ a perception-action decision …

In this paper, we propose to use deep reinforcement learning (DRL) for the task of
cooperative spectrum sensing (CSS) in a cognitive radio network. We selected a recently …

Cognitive radio (CR) technology is a promising candidate for next generation intelligent
wireless networks. The cognitive engine plays the role of the brain for the CR and the …

In Cognitive radio (CR), users take advantage of vacant licensed bands to transmit their data
as they become available. Cognitive users employ an autonomous perception-action …

Cognitive radio (CR) is a spectrum sharing technology that facilitates a hierarchal
coexistence between licensed and license-exempt users over licensed bands. One of the …

In cognitive radio (CR) networks, the cognition cycle, ie, the ability of wireless transceivers to
learn the optimal configuration meeting environmental and application requirements, is …