Cultural Algorithm based Cooperative Spectrum Sensing Optimisation in Cognitive Radio Network

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.