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PSO based Cross Layer Optimization for Primary User selection in Cognitive Radios
2014
International Journal of Future Generation Communication and Networking
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In Cognitive Radio Networks, selection of primary users (PUs) is an important process for achieving optimal performance during a session. ...
The fitness values of these objective functions in different modes and channels are investigated using MATLAB and the results shows that PSO is 70% faster than the Genetic Algorithm in terms of convergence ...
Introduction Cognitive Radio Network (CRN) is a smart wireless network which senses the surrounding radio environment and adapt its operating parameters for efficient utilization of available radio spectrum ...
doi:10.14257/ijfgcn.2014.7.3.09
fatcat:y3l7dwa6njbwheqeaxxux3lbpm
New Approaches in Cognitive Radios using Evolutionary Algorithms
2018
International Journal of Electrical and Computer Engineering (IJECE)
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Therefore, current technologies have matters presented in optimization, learning, and classification over cognitive radios where evolutionary algorithms can be presented big approaches. ...
Cognitive radio has claimed a promising technology to exploit the spectrum in an ad hoc network. ...
ACKNOWLEDGEMENTS The authors of this article wish to acknowledge Colciencias and the Universidad Distrital Francisco José de Caldas for funding resources to develop this research. ...
doi:10.11591/ijece.v8i3.pp1636-1646
fatcat:aqljovnpgncdtaw6gkmdzcse7a
A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network
2020
SN Computer Science
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To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). ...
A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. ...
artificial bee colony (EA-ABC) algorithm for cooperative spectrum sensing in a CRN. ...
doi:10.1007/s42979-020-00372-z
fatcat:jk3gcvhp3batdgwyc47hv7y54e
Recent advances on artificial intelligence and learning techniques in cognitive radio networks
2015
EURASIP Journal on Wireless Communications and Networking
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, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. ...
The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine ...
Artificial bee colony The artificial bee colony (ABC) concept was introduced by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. ...
doi:10.1186/s13638-015-0381-7
fatcat:dq6aba75obc5vlxnbaqrerlsii
Energy efficient spectrum sensing for cognitive radio network using artificial bee colony algorithm
2018
International Journal of Engineering & Technology
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In this paper Artificial Bee Colony (ABC) algorithm based optimization of energy efficiency for spectrum sensing in a Cognitive Radio Network (CRN) is implemented. ...
ABC algorithm which is an efficient optimization technique is used for optimizing energy efficiency func-tion derived for cognitive users, where energy efficiency function is derived as the dependency ...
Artificial bee colony (ABC) algorithm Inspired by the behavior of honeybee swarms for searching food sources and implementing the similar searching process for finding an optimal solution by simulating ...
doi:10.14419/ijet.v7i4.10094
fatcat:ah4wi4itnrbs7d3sixsuvzaxv4
Detection and Prevention of Primary User Emulation Attack in Cognitive Radio Networks Using Secure Hash Algorithm
2021
International Journal of Intelligent Engineering and Systems
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Cognitive radio network is enormously used to improve the spectrum utilization by enabling the dynamic spectrum access. ...
The performance of the cognitive radio network is affected due to the denial of service caused by the primary user emulation attack. ...
Elghamrawy [18] presented the hybrid Genetic Artificial Bee Colony (GABC) algorithm to optimize the spectrum usage based on the PUE attack detection and improvement in the detection probability. ...
doi:10.22266/ijies2021.0430.12
fatcat:2qcciv2gvvelvozwgrlzvacuue
Solving resource allocation problems in cognitive radio networks: a survey
2016
EURASIP Journal on Wireless Communications and Networking
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Cognitive radio networks (CRN), in their quest to become the preferred next-generation wireless communication paradigm, will depend heavily on their ability to efficiently manage the limited resources ...
The various ideas put forward by researchers to address RA problems in CRN have been somewhat diverse, and somehow, there seem to be no links that bring cohesion and clarity of purpose and ideas. ...
Acknowledgements This work was supported by the SENTECH Chair in Broadband Wireless Multimedia Communications (BWMC), a telecommunications research group in the Department of Electrical, Electronics and ...
doi:10.1186/s13638-016-0673-6
fatcat:xjptahuvvvcmph4jos7zdxmvxa
Artificial intelligence based cognitive routing for cognitive radio networks
2015
Artificial Intelligence Review
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2013 pre-print arXiv:1309.0085v1 fatcat:xaqryzzf6rhi7k3ypxr4fygrym
Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. ...
While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. ...
INTRODUCTION In cognitive radio networks (CRNs), nodes are equipped with cognitive radios (CRs) that can sense, learn, and react to changes in network conditions. ...
doi:10.1007/s10462-015-9438-6
fatcat:hi4mk5iaf5dsjgco5sgknejroq
On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches
2014
IEEE Communications Surveys and Tutorials
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security and Ant Colony Optimization (ACO)-based multipath routing have been surveyed and compared. ...
The existing well-known bio-inspired algorithms such as pulse-coupled oscillators (PCO)based synchronization, ant-and/or bee-inspired cooperation and division of labor, immune systems inspired network ...
ACKNOWLEDGMENT The authors would like to acknowledge the editor and anonymous reviewers for their critical comments, which greatly improved this paper. ...
doi:10.1109/surv.2013.062613.00014
fatcat:gil3a5vbm5abzbrnbsyfagosr4
Spectrum Assignment in Cognitive Radio Networks: A Comprehensive Survey
2013
IEEE Communications Surveys and Tutorials
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The scope of this work is to give an overview of the problem of spectrum assignment in cognitive radio networks, presenting the state-of-the-art proposals that have appeared in the literature, analyzing ...
Cognitive radio (CR) has emerged as a promising technology to exploit the unused portions of spectrum in an opportunistic manner. ...
ACKNOWLEDGMENTS The authors thank the reviewers for their useful comments which helped to improve the quality and presentation of this paper. ...
doi:10.1109/surv.2012.121112.00047
fatcat:icmjsxgednbvdcnees5i4lvaae
IEEE Access Special Section Editorial: New Waveform Design and Air-Interface for Future Heterogeneous Network Towards 5G
2020
IEEE Access
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To effectively solve this combinatorial optimization problem, a modified binary artificial bee colony algorithm is proposed. ...
The article ''An energy efficient Internet of Things network using restart artificial bee colony and wireless power transfer,'' by Zhang et al., attempts to study an IoT network containing wireless sensors ...
doi:10.1109/access.2020.3019946
fatcat:ardgft3rhbbcrgfmhqkxtvujh4
When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
[article]
2022
arXiv
pre-print
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In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML ...
based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. ...
A Genetic Algorithm was used to compensate for the Artificial Bee Colony algorithm's less than optimal exploitation of solu-tions by using crossover and mutation operations. ...
arXiv:2201.04677v1
fatcat:gb73bku37fatncamax67buwkea
Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks
2020
Mobile Information Systems
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In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). ...
The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. ...
of the system. e artificial bee colony clustering (ABCC) algorithm in [11] is competent to reduce and stabilize the energy expenditure of the cooperative users. ...
doi:10.1155/2020/8876824
fatcat:c44lapnvzfc4ff4nnllf7hn2o4
Review on Cross Layer Design for Cognitive Ad-hoc and Sensor Network
2020
IET Communications
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Introducing cognitive radio (CR) concept in AHSN, the unutilised licensed spectrum (white spaces) can be used opportunistically without disturbing licensed holders. ...
This interesting field of research gave birth to the cognitive radio (CR) model for sensors to improve spectrum efficiency without additional deployment cost. ...
learning (RL), Bayesian, support vector machine (SVM), case-based reasoning, entropy, multi-agent systems and artificial bee colony algorithm. ...
doi:10.1049/iet-com.2019.0636
fatcat:7fduwpktgjhjpishypcxttqke4
Overview on Binary Optimisation using Swarm-inspired Algorithms
2021
IEEE Access
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Based on simple rules, simple reactive agents -swarm (e.g. fish, bird, and ant)directly or indirectly exchange information to find an optimal solution. ...
Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. ...
[45] solve a spectrum allocation optimisation for cognitive radio networks (CRN). ...
doi:10.1109/access.2021.3124710
fatcat:v7heosioojg3pcwpswm3yvmrmy
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