Cognitive Radio Decision Engine Based on Binary Chaotic Particle Swarm Optimization

Cognitive radio decision engine based on particle swarm optimization is proposed. A population adaptive particle swarm optimization is also proposed to improve the convergence rate. Particle swarm optimization and population adaptive particle swarm optimization are used to adapt radio parameters respectively, and multi-carrier system is used for the performance analysis. Results show that cognitive decision engine based on binary particle swarm optimization has better convergence, precision and stability than the classic genetic algorithm, and population adaptive particle swarm optimization can further improve the performance at the initial stage of the search to meet real time requirement of cognitive radio.