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

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) users can transmit data as vacant licensed bands become available.
By using machine learning, CR users can intelligently sense channel activity and determine …

We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio
networks based on imbalanced learning techniques, which aims to resolve the skewed …

In this paper, a cooperative Secondary Network (SN) is proposed that operates under a
hybrid underlay-interweave model. The narrowband sensing problem under the interweave …

Spectrum sensing is an essential function of cognitive radio technology that can enable the
reuse of available radio resources by so-called secondary users without creating harmful …

In this paper, we present DEAP learning, an unsupervised approach for cooperative
spectrum sensing. DEAP learning uses a sparse autoencoder (SAE) to learn a useful …

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 networks (CRNs) can greatly improve the temporal and spatial spectrum
utilization by identifying and exploring spectrum holes of the licensed primary users (PUs) …

Cognitive radio (CR) can provide a promising solution to the spectrum scarcity issue for
upcoming wireless communication technologies. Cooperative spectrum sensing (CSS) is …