New Advances in Cognitive Radio Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1110

Special Issue Editor

Department of Electronics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Interests: cognitive radio networks; game theory; machine learning; information theory; cryptography; ad hoc networks; computer architecture

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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

Published Papers (1 paper)

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Research

18 pages, 1573 KiB  
Article
Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
Appl. Sci. 2023, 13(14), 8127; https://doi.org/10.3390/app13148127 - 12 Jul 2023
Cited by 1 | Viewed by 758
Abstract
In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, [...] Read more.
In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, an amplify and forward (AF) relaying technique is employed between the SU source and SU destination. In this context, the mobile end user (SU destination) is assumed to move at high vehicular speeds, whereas other nodes (SU Source, SU relays and PU) are assumed to be stationary. The proposed scheme dynamically determines the best relay for transmission based on the highest signal-to-noise (SNR) ratio by deploying selection combiner at the SU destination, thereby achieving diversity. All channels connected with the stationary nodes are modelled using Rayleigh distribution, whereas all other links connected with the mobile end user are modelled using Nakagami-m fading distribution (m<1). The outage probabilities (OPs) are obtained considering several scenarios and Monte Carlo simulation is used to verify the numerical results. The obtained results show that a variety of factors, including the number of SU relays, the severity of the fading channels, the position of the PU, the fading model, and the mobile end user speed, might influence the CRN’s performance. Full article
(This article belongs to the Special Issue New Advances in Cognitive Radio Networks)
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