Deep reinforcement learning (DRL) has been successfully used for the joint routing and
resource management in large-scale cognitive radio networks. However, it needs lots of …

The present work explores the scope of cognitive radio networks (CRNs) to support the
spectrum demand on future diverse application specific wireless services supported by long …

Cognitive Radio (CR) is a promising technology to overcome spectrum scarcity, which
currently faces lots of unsolved problems. One of the critical challenges for setting up such …

Abstract In cognitive radio, Reinforcement Learning (RL) has been widely applied to the
construction of cognition engine. However, two crucial challenges remain to be resolved …

In this paper, we propose a novel deep reinforcement learning-based quality-of-service
(QoS) routing protocol, namely DRQR, exploiting cross-layer design to establish efficient …

In this paper, we propose an efficient quality-of-service routing protocol in cognitive radio
mobile ad hoc networks (CR-MANETs), where a QoS route is formed by exploiting deep …

Cognitive radio networks (CRNs) implementing spectrum sharing between primary and
secondary users are able to provide a needed solution for the increasing spectrum scarcity …

Transmission latency minimization and energy efficiency improvement are two main
challenges in multi-hop Cognitive Radio Networks (CRN), where the knowledge of topology …

Wireless sensor networks (WSNs) play an important role in various real-time applications
such as health monitoring, security application, military applications, etc. However, these …

Abstract Cognitive Radio Networks are an emerging technology in for wireless
communication. With increasing number of wireless devices in wireless communication …