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Optimisation of Cognitive Engine Design using Cultural Algorithm


Affiliations
1 Department of Electronics Engineering, Institute of Engineering and Technology, Lucknow – 226021, Uttar Pradesh, India
2 Faculty of Communication Engineering, MCTE, Mhow – 453441, Madhya Pradesh, India
 

Objectives: To optimize the design of cognitive radio engine using Cultural Algorithm (CA). The simulated results are compared with commonly used Evolutionary Algorithms (EA) like Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Methods/Statistical Analysis: Use of CA has been proposed to find a suitable fitness score under varying channel conditions over multiple iterations. Matlab has been chosen as the platform for simulating various scenarios. An attempt has also been made to optimize the time of convergence. Findings: Simulations indicate that CA emerges as a potential candidate for designing of CR Engine (CRE) for deployment of a CR Network (CRN). Using CA, the fitness score has improved as compared to other EAs. Improvements: The algorithm shows faster convergence and improves its performance with each successive iteration.

Keywords

Cultural Algorithm, Cognitive Radios, Cognitive Engine, Environmental Parameters, Fitness Functions
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  • Optimisation of Cognitive Engine Design using Cultural Algorithm

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Authors

Suchita Shukla
Department of Electronics Engineering, Institute of Engineering and Technology, Lucknow – 226021, Uttar Pradesh, India
Abhishek Singh
Faculty of Communication Engineering, MCTE, Mhow – 453441, Madhya Pradesh, India
Atul Singh
Faculty of Communication Engineering, MCTE, Mhow – 453441, Madhya Pradesh, India
Neelam Srivastava
Faculty of Communication Engineering, MCTE, Mhow – 453441, Madhya Pradesh, India

Abstract


Objectives: To optimize the design of cognitive radio engine using Cultural Algorithm (CA). The simulated results are compared with commonly used Evolutionary Algorithms (EA) like Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Methods/Statistical Analysis: Use of CA has been proposed to find a suitable fitness score under varying channel conditions over multiple iterations. Matlab has been chosen as the platform for simulating various scenarios. An attempt has also been made to optimize the time of convergence. Findings: Simulations indicate that CA emerges as a potential candidate for designing of CR Engine (CRE) for deployment of a CR Network (CRN). Using CA, the fitness score has improved as compared to other EAs. Improvements: The algorithm shows faster convergence and improves its performance with each successive iteration.

Keywords


Cultural Algorithm, Cognitive Radios, Cognitive Engine, Environmental Parameters, Fitness Functions



DOI: https://doi.org/10.17485/ijst%2F2017%2Fv10i21%2F157005