基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎，并提出了一种种群自适应粒子群算法，利用粒子群算法调整优化无线电参数，运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法，基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能，满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎

Cognitive radio decision engine based on binary 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.