Suchita Shukla ¹, Abhishek Singh ², Atul Singh ², Neelam Srivastava ²

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

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

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Suchita Shukla ¹, Abhishek Singh ², Neelam Srivastava ¹

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

Abstract

Objectives: Optimisation of Spectrum Sensing phenomenon by improving the probability of detection using Cultural Evolutionary Algorithm (CEA) in Cognitive Radio Network (CRN). Methods/Statistical Analysis: Cultural Algorithm (CA) has been used for the first time to optimize the spectrum sensing phenomenon. The acceptance function calculation and belief space adjustment have been performed for commonly used evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). Findings: Various scenarios for calculation of the probability of detection for a fixed value of probability of false alarm have been simulated in MATLAB. The results obtained have been compared with GA and PSO under identical scenarios. Improvements: Simulations reveal that CA achieves a better probability of detection as compared to GA and PSO for a given probability of false alarm. It observed that detection probability improves with an increase in participating population set of cognitive radios.

Keywords

Cognitive Radio, Cooperative Spectrum Sensing, Cultural Algorithm, Genetic Algorithm, Particle Swarm Optimisation

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