New Advances in Cognitive Radio Networks

Dear Colleagues,

Wireless networks face challenges in the efficient utilization of spectrum technology due to the paucity of resources and fixed-spectrum allocations. The cognitive radio paradigm can bring efficiency, better utilization of bandwidth, and appropriate management of limited resources. Researchers from across the industry are exploring many avenues to make efficient use of cognitive radio networks. Recently, there is a manifold increase in applications, with machine learning being extensively utilized to improve the performance of cognitive radio networks. With the extensive use of artificial intelligence in almost all walks of life, it is imperative that such a concept can made as instrumental as possible in realizing useful cognitive radio networks. The use of artificial intelligence is an attractive choice of method for use to predict future traffic patterns in order to adapt the network according to changing conditions and parameters. Similarly, deep reinforcement learning has been extensively used for resource allocation to optimize spectrum utilization in complex environments. Important challenges in cognitive radio networks include spectrum sensing, adaptive modulation, coding, and resource allocation. The implementation of a hybrid access model and the use of game theory in collaborative and competitive environments for spectrum sharing, spectrum trading, and market-based approaches also results in improved performance, enforcing acceptable etiquettes in the use of cognitive radio networks. It is also pivotal to provide an acceptable level of security and to ensure privacy to the ever-increasing number of users and devices. In the evolution of telecommunications networks, cognitive radios play an increasingly important role in easing and establishing communication links between users and applications, especially in 5G and beyond networks. The use of multi-radio multi-channel techniques is also on the increase to deal with the scarcity of available channels and thus improve spectral efficiency. Another important aspect of cognitive radios is the careful use of energy resources, especially for battery-powered mobile devices. Their application to smart grids is also an attractive choice, where the cognitive networks can assist in real-time monitoring and control energy distribution according to the changing requirements. The resources can further be efficiently utilized by processing data closer to the source by using the edge computing setup, where the researchers explore the possibility of using cognitive radios to perform data processing and analysis in real-time decision making. The application of cognitive radio technology to the Internet of Things is beneficial, as multiple devices may compete for limited spectrum resources, and can make a considerable contribution in the area of unmanned aerial vehicles to perform surveillance, inspection, and delivery. In addition, with the popularity of cloud computing, the role of cognitive radio networks can improve its performance and scalability for efficient spectrum management. In unexpected and disastrous situations, the use of cognitive radio networks is valuable, and it is generally vital that the importance of public safety applications be kept in view.

We invite researchers to submit original research articles that address recent trends in cognitive radio networks. The potential topics include, but are not limited to:

Machine learning and artificial intelligence, spectrum sensing, spectrum sharing, dynamic spectrum access, game theory, security and privacy, 5G and beyond, Internet of Things, multi-radio multi-channel, energy efficiency, edge computing, unmanned aerial vehicles, cloud computing, smart grid, radio environment mapping, public safety, and a variety of other relevant topics.

Prof. Dr. Hasan Mahmood
Guest Editor

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• machine learning and artificial intelligence
• spectrum sensing
• spectrum sharing
• game theory
• security and privacy
• 5G and beyond
• Internet of Things
• energy efficiency
• edge computing
• unmanned aerial vehicles
• cloud computing
• smart grid
• public safety